You searched for subject:(Text based Image Retrieval).
Showing records 1 – 30 of
54153 total matches.
◁ [1] [2] [3] [4] [5] … [1806] ▶
1.
Zheng, Shaochen.
Investigation of the Effectiveness of Applying Information Retrieval Techniques to Text-based Image Retrieval Methods.
Degree: MS, Computer Science, 2016, University of Windsor
URL: https://scholar.uwindsor.ca/etd/5924 
► With advances in computer technology, there has been an explosion in the amount of digital images being generated. It is of importance to retrieve…
(more)
▼ With advances in computer technology, there has been an explosion in the amount of digital images being generated. It is of importance to retrieve images accurately and e ciently.
Text-
based Image Retrieval (TBIR) methods are popular and practical in extensive applications and have been developed in the past decades. Since the process of TBIR is similar to Information
Retrieval (IR), di erent techniques were adopted from IR and utilized to improve the performance of TBIR methods. In this thesis, we focus on three IR techniques which are Term Frequency - Inverse Document Frequency (TF-IDF), Vector Space Model (VSM) and Cosine Coe cient Similarity (CCS) measure. These three techniques have been utilized in TBIR methods together and separately and can e ectively improve the performance of TBIR methods. However, to the best of our knowledge, the TBIR methods that utilized the three techniques together are hybrid approaches, only the performance of Contentbased
Image Retrieval (CBIR) and TBIR hybrid methods are evaluated by the authors. Consequently, the e ectiveness of applying these three IR techniques to TBIR methods is investigated by comparing the
retrieval results of an experimental TBIR system in 2 di erent modes: one is the system implemented with only TF-IDF technique (Mode 2) and the other one with all three techniques (Mode 1).
Based on the experiment results, the performance of the experimental TBIR system implemented with the three IR techniques is relatively ideal. In most cases, the average precision is above 80% on the IAPR TC-12
image database. Moreover, we also investigate how the repeated index terms a ect the performance of TBIR methods by comparing the top 5 retrieved images' rankings generated by the 2 modes of the experimental TBIR system. According to the experiment results, we found that adding VSM and CCS measure to the experimental TBIR system that is only implemented with TF-IDF technique could improve its performance in terms of ranking accuracy in most cases when images' annotations contain repeated index terms that match the query.
Advisors/Committee Members: Wu, Dan.
Subjects/Keywords: Image Retrieval; Informational Retrieval; IR; TBIR; Text-based Image Retrieval
…include
Content-based Image Retrieval (CBIR)[6], Hashing-Based Image… …annotate an
image with keywords and to retrieve images based on text queries. In this approach… …image contains repeated index terms. . . . . . . . . . .
45
22
The retrieval result of the… …in recent
years, a variety of image retrieval methods thusly have been proposed, which… …Retrieval[51], Textbased Image Retrieval (TBIR)[39], etc. Among all…
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zheng, S. (2016). Investigation of the Effectiveness of Applying Information Retrieval Techniques to Text-based Image Retrieval Methods. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/5924
Chicago Manual of Style (16th Edition):
Zheng, Shaochen. “Investigation of the Effectiveness of Applying Information Retrieval Techniques to Text-based Image Retrieval Methods.” 2016. Masters Thesis, University of Windsor. Accessed January 24, 2021.
https://scholar.uwindsor.ca/etd/5924.
MLA Handbook (7th Edition):
Zheng, Shaochen. “Investigation of the Effectiveness of Applying Information Retrieval Techniques to Text-based Image Retrieval Methods.” 2016. Web. 24 Jan 2021.
Vancouver:
Zheng S. Investigation of the Effectiveness of Applying Information Retrieval Techniques to Text-based Image Retrieval Methods. [Internet] [Masters thesis]. University of Windsor; 2016. [cited 2021 Jan 24].
Available from: https://scholar.uwindsor.ca/etd/5924.
Council of Science Editors:
Zheng S. Investigation of the Effectiveness of Applying Information Retrieval Techniques to Text-based Image Retrieval Methods. [Masters Thesis]. University of Windsor; 2016. Available from: https://scholar.uwindsor.ca/etd/5924
Montana State University
2.
Banda, Juan.
Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis.
Degree: PhD, College of Engineering, 2011, Montana State University
URL: https://scholarworks.montana.edu/xmlui/handle/1/865
► With the launch of NASA's Solar Dynamics Observatory mission, a whole new age of high-quality solar image analysis was started. With the generation of over…
(more)
▼ With the launch of NASA's Solar Dynamics Observatory mission, a whole new age of high-quality solar
image analysis was started. With the generation of over 1.5 Terabytes of solar images, per day, that are ten times higher resolution than high-definition television, the task of analyzing them by scientists by hand is simply impossible. The storage of all these images becomes a second problem of importance due to the fact that there is only one full copy of this repository in the world, therefore an alternate and compressed representation of these images is of vital importance. Current automated
image processing approaches in solar physics are entirely dedicated to analyze individual types of solar phenomena and do not allow researchers to conveniently query the whole Solar Dynamics Observatory repository for similar images of their interests. We developed a Content-
based Image Retrieval system that can automatically analyze and retrieve multiple different types of solar phenomena, this will fundamentally change the way researchers look for solar images in a similar way as Google changed the way people searched the internet. During the development of our system, we created a framework that would allow researchers to tweak and develop their own content-
based image retrieval systems for different domain-specific applications with great ease and a deeper understanding of the representation of domain-specific
image data. This framework incorporates many different aspects of
image processing and information
retrieval such as:
image parameter extraction for reduced representation of solar images,
image parameter evaluation for validation of
image parameters used, evaluation of multiple dissimilarity measures for more accurate data analysis, analyses of dimensionality reduction methods to help reduce storage and processing costs, and indexing and
retrieval algorithms for faster and more efficient search. The capabilities of this framework have never been available together as an open source and comprehensive software package. With these unique capabilities, we achieved a higher level of knowledge of our solar data and validated each of our steps into the creation of our solar content-
based image retrieval system with an exhaustive evaluation. The contributions of our framework will allow researchers to tweak and develop new content-
based image retrieval systems for other domains (e.g astronomy, medical field) and will allow the migration of astrophysics research from the individual analysis of solar phenomenon into larger-scale data analyses.
Advisors/Committee Members: Chairperson, Graduate Committee: Rafal A. Angryk (advisor).
Subjects/Keywords: Content-based image retrieval.; Helioseismology.
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Banda, J. (2011). Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis. (Doctoral Dissertation). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/865
Chicago Manual of Style (16th Edition):
Banda, Juan. “Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis.” 2011. Doctoral Dissertation, Montana State University. Accessed January 24, 2021.
https://scholarworks.montana.edu/xmlui/handle/1/865.
MLA Handbook (7th Edition):
Banda, Juan. “Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis.” 2011. Web. 24 Jan 2021.
Vancouver:
Banda J. Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis. [Internet] [Doctoral dissertation]. Montana State University; 2011. [cited 2021 Jan 24].
Available from: https://scholarworks.montana.edu/xmlui/handle/1/865.
Council of Science Editors:
Banda J. Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis. [Doctoral Dissertation]. Montana State University; 2011. Available from: https://scholarworks.montana.edu/xmlui/handle/1/865
Anna University
3.
Baulkani S.
Some studies on multifeature Analysis and enhancement for
Content based image retrieval;.
Degree: Some studies on multifeature Analysis and enhancement
for Content based image retrieval, 2015, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/44194
► In computer vision and image processing research the aim is to derive newlinebetter tools that give us different perspectives on the same image allowing us…
(more)
▼ In computer vision and image processing research
the aim is to derive newlinebetter tools that give us different
perspectives on the same image allowing us newlineto understand not
only its content but also its meaning and significance newlineImage
processing cannot compete with the human eye in terms of accuracy
newlinebut it can outperform easily in observational consistency
and in its ability to newlinecarry out detailed mathematical
operations Also on simple or structured newlinetasks computing
solutions can be reliable consistent and inexpensive Over
newlinethe years image processing research has broadened from basic
pixel based newlinelow level operations to high level analyses
which include the use of Artificial newlineIntelligent techniques
for image interpretation and understanding These new
newlinetechnologies are being developed to gain a better semantic
understanding of newlineimages based on the relationship between
their components context and newlinehistory if they are a part of a
sequence and a priori knowledge is gained from newlinea range of
sources newlineIn the case of Content Based Image Retrieval CBIR it
is the content newlinethat defines the object category In a
majority of image analysis applications newlineobject recognition
in images is a fundamental step Such research can be newlineeasily
categorized as that dealing with well defined objects and that
newlinedealing with natural objects Well defined objects are mostly
found in newlineresearch dealing with industrial applications of
machine vision and automated newlinetarget detection whereas
natural objects are dealt with in remote sensing and newlineother
applications newline newline
appendix p206-216, reference
p217-236.
Advisors/Committee Members: Ganesan L.
Subjects/Keywords: Content Based Image Retrieval; Image processing
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
S, B. (2015). Some studies on multifeature Analysis and enhancement for
Content based image retrieval;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/44194
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
S, Baulkani. “Some studies on multifeature Analysis and enhancement for
Content based image retrieval;.” 2015. Thesis, Anna University. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/44194.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
S, Baulkani. “Some studies on multifeature Analysis and enhancement for
Content based image retrieval;.” 2015. Web. 24 Jan 2021.
Vancouver:
S B. Some studies on multifeature Analysis and enhancement for
Content based image retrieval;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/44194.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
S B. Some studies on multifeature Analysis and enhancement for
Content based image retrieval;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/44194
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
4.
Ranasinghe Arachchilage, Ishan P, Mr.
Building an Efficient Content Based Image Retrieval System by Changing the Database Structure.
Degree: Computer Science: M.S., Computer Science and Information Technology, 2016, St. Cloud State University
URL: https://repository.stcloudstate.edu/csit_etds/15
► Content Based Image Retrieval (CBIR) is still a major research area due to its complexity and the growth of the image databases. Color Based…
(more)
▼ Content
Based Image Retrieval (CBIR) is still a major research area due to its complexity and the growth of the
image databases. Color
Based Image Retrieval is one of the major
retrieval methods in Content
Based Image Retrieval systems. At present, researchers combine
image retrieval techniques to get more accurate results. With the large
image databases,
image retrieval is still a challenging area and the efficiency of the
image retrieval techniques still need to be considered. For this purpose, a comparative study of
image retrieval techniques has been discussed in this paper. In addition, an efficient method is presented which aids to retrieve images by storing an intermediate result of the process in the database. To compare the query
image and the images in the database, Euclidean distance, Normalized Cross Correlation distance and Histogram Intersection distance are taken as distance measures. Experimental results demonstrate Histogram Intersection distance is better than the other two methods. The intermediate result was stored using an event in the system. By making minor modifications to the proposed system, it creates a possibility for the user to add images to the database just by clicking on a button. Thus, the user can expand his/her database on his/her own will. Results show a significant improvement of performance in the proposed method.
Advisors/Committee Members: Jayantha Herath, Ezzat Kirmani, Susantha Herath.
Subjects/Keywords: Content Based Image Retrieval; CBIR; Image Comparison; Color Histogram; Image Retrieval; Image Processing
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ranasinghe Arachchilage, Ishan P, M. (2016). Building an Efficient Content Based Image Retrieval System by Changing the Database Structure. (Masters Thesis). St. Cloud State University. Retrieved from https://repository.stcloudstate.edu/csit_etds/15
Chicago Manual of Style (16th Edition):
Ranasinghe Arachchilage, Ishan P, Mr. “Building an Efficient Content Based Image Retrieval System by Changing the Database Structure.” 2016. Masters Thesis, St. Cloud State University. Accessed January 24, 2021.
https://repository.stcloudstate.edu/csit_etds/15.
MLA Handbook (7th Edition):
Ranasinghe Arachchilage, Ishan P, Mr. “Building an Efficient Content Based Image Retrieval System by Changing the Database Structure.” 2016. Web. 24 Jan 2021.
Vancouver:
Ranasinghe Arachchilage, Ishan P M. Building an Efficient Content Based Image Retrieval System by Changing the Database Structure. [Internet] [Masters thesis]. St. Cloud State University; 2016. [cited 2021 Jan 24].
Available from: https://repository.stcloudstate.edu/csit_etds/15.
Council of Science Editors:
Ranasinghe Arachchilage, Ishan P M. Building an Efficient Content Based Image Retrieval System by Changing the Database Structure. [Masters Thesis]. St. Cloud State University; 2016. Available from: https://repository.stcloudstate.edu/csit_etds/15
5.
Shrivastava, Nishant.
An effective content based image retrieval system using
relevance search and low level features;.
Degree: Computer Science Engineering, 2014, Jaypee University of Engineering and Technology, Guna
URL: http://shodhganga.inflibnet.ac.in/handle/10603/19933
► The enormous growth in the internet and multimedia technology has generated a huge amount of data in the form of images, videos, and audio. This…
(more)
▼ The enormous growth in the internet and multimedia
technology has generated a huge amount of data in the form of
images, videos, and audio. This has created the demand of systems
which can store and retrieve multimedia data like images in an
effective and efficient manner. Content Based Image Retrieval
(CBIR) is the searching, navigation and retrieval of images based
on their visual content. Visual content of image include color,
texture, shape and spatial location of objects depicted in the
image. The low level features like color, texture and shape have
limited capability for describing visual contents of an image.
Therefore semantic gap is observed between visual interpretation
and representation of images with low level features. newline
Researchers all over the world are trying to fill this semantic gap
using low level features and their combinations. This thesis is a
next step in this series. A number of problems in existing low
level features and retrieval systems are identified and their
solutions are proposed. newline Generally the accuracy of a Content
Based Image Retrieval system decreases as the number and variety of
images increases in the database. Similar images depicting
different semantic concepts may be retrieved as a result. In
addition, the extraction of shape features requires accurate
segmentation of images. However, segmenting image itself is an open
problem; therefore extraction of shape features is not much
reliable. A CBIR using multistage retrieval strategy is proposed
here to deal with these problems. newline Low level features of an
image can be local and global. Global features are extracted using
whole image and local features are extracted from a local region or
part of image. Local features based CBIR systems are called Region
Based Image Retrieval systems (RBIR). Accuracy of a RBIR system is
higher than corresponding global CBIR in general. The traditional
RBIR systems face the problem of precise query formulation and
higher response time.
List of Publication
Advisors/Committee Members: Tyagi , Vipin.
Subjects/Keywords: Content based image retrieval; Image retrieval based on region of
interest; Local structure pattern
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shrivastava, N. (2014). An effective content based image retrieval system using
relevance search and low level features;. (Thesis). Jaypee University of Engineering and Technology, Guna. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/19933
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shrivastava, Nishant. “An effective content based image retrieval system using
relevance search and low level features;.” 2014. Thesis, Jaypee University of Engineering and Technology, Guna. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/19933.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shrivastava, Nishant. “An effective content based image retrieval system using
relevance search and low level features;.” 2014. Web. 24 Jan 2021.
Vancouver:
Shrivastava N. An effective content based image retrieval system using
relevance search and low level features;. [Internet] [Thesis]. Jaypee University of Engineering and Technology, Guna; 2014. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/19933.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shrivastava N. An effective content based image retrieval system using
relevance search and low level features;. [Thesis]. Jaypee University of Engineering and Technology, Guna; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/19933
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Anna University
6.
Felci rajam I.
Adaptive invariant content based Image retrieval using
semantic Learning;.
Degree: Adaptive invariant content based Image retrieval using
semantic Learning, 2015, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/38881
► With the extensive growth of image databases in the digital world newlineefficient image retrieval has gained much importance Content Based Image newlineRetrieval CBIR is the…
(more)
▼ With the extensive growth of image databases in the
digital world newlineefficient image retrieval has gained much
importance Content Based Image newlineRetrieval CBIR is the
retrieval of relevant images from the image database newlinebased
on the visual content of the image which consists of the low level
newlinevisual features such as color texture and shape The
performance of most of newlinethe CBIR systems is limited by the
low level visual features since they newlinecannot adequately
express the high level human perception This is the newline
semantic gap problem To reduce the semantic gap semantic knowledge
newlineincorporation into the content based image retrieval system
has become very newlineimportant This is known as semantic content
based image retrieval SCBIR newlineMany recent research works are
going on in the field of SCBIR newlineThis research work aims to
develop a semantic learning scheme newlinefor predicting the
semantic concept of the query image The support vector
newlinemachine SVM a supervised machine learning algorithm is used
in the newlineconstruction of the semantic learning scheme The SVM
binary decision tree newline SVM BDT is constructed using the
semantic templates of each of the newlinesemantic concept Using the
low level features of the query image the newlineSVM BDT predicts
the semantic concept of the query image newline
newline
referencec p162-176.
Advisors/Committee Members: Valli S.
Subjects/Keywords: Content Based Image Retrieval; Semantic content based image retrieval; Support vector machine
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
I, F. r. (2015). Adaptive invariant content based Image retrieval using
semantic Learning;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/38881
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
I, Felci rajam. “Adaptive invariant content based Image retrieval using
semantic Learning;.” 2015. Thesis, Anna University. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/38881.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
I, Felci rajam. “Adaptive invariant content based Image retrieval using
semantic Learning;.” 2015. Web. 24 Jan 2021.
Vancouver:
I Fr. Adaptive invariant content based Image retrieval using
semantic Learning;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38881.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
I Fr. Adaptive invariant content based Image retrieval using
semantic Learning;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38881
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Anna University
7.
Mythili C.
Efficient technique for color image segmentation towards
the enhancement of image retrieval;.
Degree: Efficient technique for color image segmentation
towards the enhancement of image retrieval, 2015, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/49413
► Content Based Image Retrieval CBIR system performs search and retrieval operations in image database that has been developed in recent years CBIR has a very…
(more)
▼ Content Based Image Retrieval CBIR system performs
search and retrieval operations in image database that has been
developed in recent years CBIR has a very vital impact in web
application The main objective of this thesis work is to increase
the accuracy level of image retrieval Since the image database is
very huge more attention has to be given during retrieval process
in i the quality of the image ii features that are to be selected
for newlineimage retrieval iii object detection methodology and iv
enhancement in the retrieval process The above said processes are
analyzed in four chapters and are as follows Since the retrieval
process is based on web applications where the color images are
available on the internet reduction of additive noise for digital
color images is important and is performed in chapter 3 From the
literature till recent publications it is found that wavelet based
fuzzy filter suppresses the noise very well by keeping its features
intact eg edge color components distances and so on The visual
results illustrate that the wavelet newlinebased fuzzy filter
method restores the original color components distances much better
than other filters like median wiener linear smoothing filters and
so on In this research work wavelet based fuzzy filter is used
newline
Advisors/Committee Members: Kavitha V.
Subjects/Keywords:
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
C, M. (2015). Efficient technique for color image segmentation towards
the enhancement of image retrieval;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/49413
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
C, Mythili. “Efficient technique for color image segmentation towards
the enhancement of image retrieval;.” 2015. Thesis, Anna University. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/49413.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
C, Mythili. “Efficient technique for color image segmentation towards
the enhancement of image retrieval;.” 2015. Web. 24 Jan 2021.
Vancouver:
C M. Efficient technique for color image segmentation towards
the enhancement of image retrieval;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/49413.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
C M. Efficient technique for color image segmentation towards
the enhancement of image retrieval;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/49413
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
NSYSU
8.
Huang, Yen-Ren.
Retrieval by spatial similarity based on interval neighbor group.
Degree: Master, Computer Science and Engineering, 2008, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-122600
► The objective of the present work is to employ a multiple-instance learning image retrieval system by incorporating a spatial similarity measure. Multiple-Instance learning is a…
(more)
▼ The objective of the present work is to employ a multiple-instance learning
image retrieval system by incorporating a spatial similarity measure. Multiple-Instance learning is a way of modeling ambiguity in supervised learning given multiple examples. From a small collection of positive and negative example images, semantically relevant concepts can be derived automatically and employed to retrieve images from an
image database. The degree of similarity between two spatial relations is linked to the distance between the associated nodes in an Interval Neighbor Group (ING). The shorter the distance, the higher degree of similarity, while a longer one, a lower degree of similarity. Once all the pairwise similarity values are derived, an ensemble similarity measure will then integrate these pairwise similarity assessments and give an overall similarity value between two images. Therefore, images in a database can be quantitatively ranked according to the degree of ensemble similarity with the query
image. Similarity
retrieval method evaluates the ensemble similarity
based on the spatial relations and common objects present in the maximum common subimage between the query and a database
image are considered. Therefore, reliable spatial relation features extracted from the
image, combined with a multiple-instance learning paradigm to derive relevant concepts, can produce desirable
retrieval results that better match userâs expectation.
In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an
image database are performed, namely, the proposed RSS-ING scheme v.s. 2D Be-string similarity method, and single-instance vs. multiple-instance learning. The performance in terms of similarity curves, execution time and memory space requirement show favorably for the proposed multiple-instance spatial similarity-
based approach.
Advisors/Committee Members: Shuenn-Ren Cheng (chair), Jung-Jae Chao (chair), Inn-Chyn Her (chair), John Y. Chiang (committee member), Chung-Nan Lee (chair).
Subjects/Keywords: Multiple-instance learning; Content-based image retrieval; Retrieval by spatial similarity
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Huang, Y. (2008). Retrieval by spatial similarity based on interval neighbor group. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-122600
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Huang, Yen-Ren. “Retrieval by spatial similarity based on interval neighbor group.” 2008. Thesis, NSYSU. Accessed January 24, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-122600.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Huang, Yen-Ren. “Retrieval by spatial similarity based on interval neighbor group.” 2008. Web. 24 Jan 2021.
Vancouver:
Huang Y. Retrieval by spatial similarity based on interval neighbor group. [Internet] [Thesis]. NSYSU; 2008. [cited 2021 Jan 24].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-122600.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Huang Y. Retrieval by spatial similarity based on interval neighbor group. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0723108-122600
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Anna University
9.
Ramamurthy B.
Certain approaches on content based medical image retrieval CBMIR using visual features;.
Degree: Certain approaches on content based medical image retrieval CBMIR using visual features, 2015, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/41999
► Diagnostic radiology demands accurate interpretation of complex signals in medical images Content based medical image retrieval CBMIR techniques are valuable to radiologists in assessing medical…
(more)
▼ Diagnostic radiology demands accurate interpretation of complex signals in medical images Content based medical image retrieval CBMIR techniques are valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support In general a Content Based Image Retrieval CBIR system is an extension of the traditional text based informational retrieval system However, the techniques and approaches used in CBIR have diverged from traditional text based retrieval systems and CBIR has now grown into separate research discipline in its own right Syntactically the text is one dimensional while an image is two dimensional Most of the text based information retrieval IR systems such as Yahoo Lycos and Google have achieved great success for indexing and querying web sites In contrast effective and precise content based image retrieval based on low level features such as texture and shape is still remains to be an open problem because of the extreme difficulty in image understanding
newlineFor the last two decades content based image retrieval has been a vital area of research An enormous publication in this period reflects diversity among the proposed solutions Mostly CBIR has been proved successful in non medical domains among which some with relevance to medical domain have been taken for literature review in this work Radiology images pose different challenges compared to images in the consumer
newline
newline
reference p161-177.
Advisors/Committee Members: Chandran K R.
Subjects/Keywords: Content based medical image retrieval; Information retrieval; Yahoo Lycos and Google
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
B, R. (2015). Certain approaches on content based medical image retrieval CBMIR using visual features;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/41999
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
B, Ramamurthy. “Certain approaches on content based medical image retrieval CBMIR using visual features;.” 2015. Thesis, Anna University. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/41999.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
B, Ramamurthy. “Certain approaches on content based medical image retrieval CBMIR using visual features;.” 2015. Web. 24 Jan 2021.
Vancouver:
B R. Certain approaches on content based medical image retrieval CBMIR using visual features;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/41999.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
B R. Certain approaches on content based medical image retrieval CBMIR using visual features;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/41999
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Montana State University
10.
Schuh, Michael Arthur.
High-dimensional data indexing with applications.
Degree: PhD, College of Engineering, 2015, Montana State University
URL: https://scholarworks.montana.edu/xmlui/handle/1/9215
► The indexing of high-dimensional data remains a challenging task amidst an active and storied area of computer science research that impacts many far-reaching applications. At…
(more)
▼ The indexing of high-dimensional data remains a challenging task amidst an active and storied area of computer science research that impacts many far-reaching applications. At the crossroads of databases and machine learning, modern data indexing enables information
retrieval capabilities that would otherwise be impractical or near impossible to attain and apply. One such useful
retrieval task in our increasingly data-driven world is the k-nearest neighbor (k-NN) search, which returns the k most similar items in a dataset to the search query provided. While the k-NN concept was popularized in every-day use through the sorted (ranked) results of online
text-
based search engines like Google, multimedia applications are rapidly becoming the new frontier of research. This dissertation advances the current state of high-dimensional data indexing with the creation of a novel index named ID* (\ID Star").
Based on extensive theoretical and empirical analyses, we discuss important challenges associated with high dimensional data and identify several shortcomings of existing indexing approaches and methodologies. By further mitigating against the negative effects of the curse of dimensionality, we are able to push the boundary of effective k-NN
retrieval to a higher number of dimensions over much larger volumes of data. As the foundations of the ID* index, we developed an open-source and extensible distance-
based indexing framework predicated on the basic concepts of the popular iDistance index, which utilizes an internal B
+B
-tree for efficient one-dimensional data indexing. Through the addition of several new heuristic-guided algorithmic improvements and hybrid indexing extensions, we show that our new ID* index can perform significantly better than several other popular alternative indexing techniques over a wide variety of synthetic and real-world data. In addition, we present applications of our ID* index through the use of k-NN queries in Content-
Based Image Retrieval (CBIR) systems and machine learning classification. An emphasis is placed on the NASA sponsored interdisciplinary research goal of developing a CBIR system for large-scale solar
image repositories. Since such applications rely on fast and effective k-NN queries over increasingly large-scale and high-dimensional datasets, it is imperative to utilize an efficient data indexing strategy such as the ID* index.
Advisors/Committee Members: Chairperson, Graduate Committee: Rockford J. Ross, Rafal A. Angryk (co-chair) (advisor).
Subjects/Keywords: Indexing.; Nearest neighbor analysis (Statistics).; Information retrieval.; Content-based image retrieval.
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Schuh, M. A. (2015). High-dimensional data indexing with applications. (Doctoral Dissertation). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/9215
Chicago Manual of Style (16th Edition):
Schuh, Michael Arthur. “High-dimensional data indexing with applications.” 2015. Doctoral Dissertation, Montana State University. Accessed January 24, 2021.
https://scholarworks.montana.edu/xmlui/handle/1/9215.
MLA Handbook (7th Edition):
Schuh, Michael Arthur. “High-dimensional data indexing with applications.” 2015. Web. 24 Jan 2021.
Vancouver:
Schuh MA. High-dimensional data indexing with applications. [Internet] [Doctoral dissertation]. Montana State University; 2015. [cited 2021 Jan 24].
Available from: https://scholarworks.montana.edu/xmlui/handle/1/9215.
Council of Science Editors:
Schuh MA. High-dimensional data indexing with applications. [Doctoral Dissertation]. Montana State University; 2015. Available from: https://scholarworks.montana.edu/xmlui/handle/1/9215
Universidade Nova
11.
Delgado, Diogo Miguel Melo.
Automated illustration of multimedia stories.
Degree: 2010, Universidade Nova
URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/4478
► Submitted in part fulfillment of the requirements for the degree of Master in Computer Science
We all had the problem of forgetting about what we…
(more)
▼ Submitted in part fulfillment of the requirements for the degree of Master in Computer Science
We all had the problem of forgetting about what we just read a few sentences before. This comes from the problem of attention and is more common with children and the elderly. People feel either bored or distracted by something more interesting. The
challenge is how can multimedia systems assist users in reading and remembering
stories? One solution is to use pictures to illustrate stories as a mean to captivate ones interest as it either tells a story or makes the viewer imagine one. This thesis researches the problem of automated story illustration as a method to increase the readers’ interest and attention. We formulate the hypothesis that an automated multimedia system can help users in reading a story by stimulating their reading memory with adequate visual illustrations.
We propose a framework that tells a story and attempts to capture the readers’
attention by providing illustrations that spark the readers’ imagination. The framework
automatically creates a multimedia presentation of the news story by (1) rendering news text in a sentence by-sentence fashion, (2) providing mechanisms to select the best illustration for each sentence and (3) select the set of illustrations that guarantees the best
sequence. These mechanisms are rooted in image and text retrieval techniques. To further improve users’ attention, users may also activate a text-to-speech functionality according to their preference or reading difficulties. First experiments show how Flickr images can illustrate BBC news articles and provide a better experience to news readers.
On top of the illustration methods, a user feedback feature was implemented to perfect the illustrations selection. With this feature users can aid the framework in
selecting more accurate results.
Finally, empirical evaluations were performed in order to test the user interface,image/sentence association algorithms and users’ feedback functionalities. The respective results are discussed.
Advisors/Committee Members: Magalhães, João, Correia, Nuno.
Subjects/Keywords: Story illustration; Text analysis; Informational retrieval; Image-text association; Vector space model; User feedback
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Delgado, D. M. M. (2010). Automated illustration of multimedia stories. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/4478
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Delgado, Diogo Miguel Melo. “Automated illustration of multimedia stories.” 2010. Thesis, Universidade Nova. Accessed January 24, 2021.
http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/4478.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Delgado, Diogo Miguel Melo. “Automated illustration of multimedia stories.” 2010. Web. 24 Jan 2021.
Vancouver:
Delgado DMM. Automated illustration of multimedia stories. [Internet] [Thesis]. Universidade Nova; 2010. [cited 2021 Jan 24].
Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/4478.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Delgado DMM. Automated illustration of multimedia stories. [Thesis]. Universidade Nova; 2010. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/4478
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Brigham Young University
12.
Christiansen, Ammon J.
Finding Relevant PDF Medical Journal Articles by the Content of Their Figures as well as Their Text.
Degree: MS, 2007, Brigham Young University
URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1871&context=etd
► This work addresses the need for an alternative to keyword-based search for sifting through large PDF medical journal article document collections for literature review…
(more)
▼ This work addresses the need for an alternative to keyword-based search for sifting through large PDF medical journal article document collections for literature review purposes. Despite users' best efforts to form precise and accurate queries, it is often difficult to guess the right keywords to find all the related articles while finding a minimum number of unrelated ones. Failure during literature review to find relevant, related research results in wasted research time and effort in addition to missing significant work in the related area which could affect the quality of the research work being conducted. The purpose of this work is to explore the benefits of a retrieval system for professional journal articles in PDF format that supports hybrid queries composed of both text and images. PDF medical journal articles contain formatting and layout information that imply the structure and organization of the document. They also contain figures and tables rich with content and meaning. Stripping a PDF into “full-text” for indexing purposes disregards these important features. Specifically, this work investigated the following: (1) what effect the incorporation of a document's embedded figures into the query (in addition to its text) has on retrieval performance (precision) compared to plain keyword-based search; (2) how current text-based document-query similarity methods can be enhanced by using formatting and font-size information as a structure and organization model for a PDF document; (3) whether to use the standard Euclidean distance function or the matrix distance function for content-based image retrieval; (4) how to convert a PDF into a structured, formatted, reflowable XML representation given a pure-layout PDF document; (5) what document views (such as a term frequency cloud, a document outline, or a document's figures) would help users wade through search results to quickly select those that are worth a closer look. While the results of the experiments were unexpectedly worse than their baselines of comparison (see the conclusion for a summary), the experimental methods are very valuable in showing others what directions have already been pursued and why they did not work and what remaining problems need to be solved in order to achieve the goal of improving literature review through use of a hybrid text and image retrieval system.
Subjects/Keywords: content-based image retrieval; content-based document retrieval; evaluation; modified tf-idf; document structure; document understanding; hybrid text and image retrieval system; Electrical and Computer Engineering
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Christiansen, A. J. (2007). Finding Relevant PDF Medical Journal Articles by the Content of Their Figures as well as Their Text. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1871&context=etd
Chicago Manual of Style (16th Edition):
Christiansen, Ammon J. “Finding Relevant PDF Medical Journal Articles by the Content of Their Figures as well as Their Text.” 2007. Masters Thesis, Brigham Young University. Accessed January 24, 2021.
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1871&context=etd.
MLA Handbook (7th Edition):
Christiansen, Ammon J. “Finding Relevant PDF Medical Journal Articles by the Content of Their Figures as well as Their Text.” 2007. Web. 24 Jan 2021.
Vancouver:
Christiansen AJ. Finding Relevant PDF Medical Journal Articles by the Content of Their Figures as well as Their Text. [Internet] [Masters thesis]. Brigham Young University; 2007. [cited 2021 Jan 24].
Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1871&context=etd.
Council of Science Editors:
Christiansen AJ. Finding Relevant PDF Medical Journal Articles by the Content of Their Figures as well as Their Text. [Masters Thesis]. Brigham Young University; 2007. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1871&context=etd
University of North Texas
13.
Jeong, Ki Tai.
A Common Representation Format for Multimedia Documents.
Degree: 2002, University of North Texas
URL: https://digital.library.unt.edu/ark:/67531/metadc3336/
► Multimedia documents are composed of multiple file format combinations, such as image and text, image and sound, or image, text and sound. The type of…
(more)
▼ Multimedia documents are composed of multiple file format combinations, such as
image and
text,
image and sound, or
image,
text and sound. The type of multimedia document determines the form of analysis for knowledge architecture design and
retrieval methods. Over the last few decades, theories of
text analysis have been proposed and applied effectively. In recent years, theories of
image and sound analysis have been proposed to work with
text retrieval systems and progressed quickly due in part to rapid progress in computer processing speed.
Retrieval of multimedia documents formerly was divided into the categories of
image and
text, and
image and sound. While standard
retrieval process begins from
text only, methods are developing that allow the
retrieval process to be accomplished simultaneously using
text and
image. Although
image processing for feature extraction and
text processing for term extractions are well understood, there are no prior methods that can combine these two features into a single data structure. This dissertation will introduce a common representation format for multimedia documents (CRFMD) composed of both images and
text. For
image and
text analysis, two techniques are used: the Lorenz Information Measurement and the Word Code. A new process named Jeong's Transform is demonstrated for extraction of
text and
image features, combining the two previous measurements to form a single data structure. Finally, this single data measurements to form a single data structure. Finally, this single data structure is analyzed by using multi-dimensional scaling. This allows multimedia objects to be represented on a two-dimensional graph as vectors. The distance between vectors represents the magnitude of the difference between multimedia documents. This study shows that
image classification on a given test set is dramatically improved when
text features are encoded together with
image features. This effect appears to hold true even when the available
text is diffused and is not uniform with the
image features. This
retrieval system works by representing a multimedia document as a single data structure. CRFMD is applicable to other areas of multimedia document
retrieval and processing, such as medical
image retrieval, World Wide Web searching, and museum collection
retrieval.
Advisors/Committee Members: Rorvig, Mark E., O'Connor, Brian Clark, Ji, Minhe, Hastings, Samantha Kelly.
Subjects/Keywords: Multimedia systems.; Document imaging systems.; Information retrieval.; Content-based image retrieval; text and image; Lorenz information measurement; single data structure
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
Addis Ababa University
14.
Estifanos, Gashaw.
Shape Sensitive Salient-object based Image Query
.
Degree: 2008, Addis Ababa University
URL: http://etd.aau.edu.et/dspace/handle/123456789/2702
► The availability of images in electronic format has resulted in large number of digital images being used for different applications. The growth in the usage…
(more)
▼ The availability of images in electronic format has resulted in large number of digital
images being used for different applications. The growth in the usage and availability of
digital images calls for an efficient means of
image data management. This has led to the
development of content
based image retrieval (CBIR) systems, which uses visual
contents of an
image such as color, texture or shape for querying
image databases. CBIR
is well researched and there are a large number of
image retrieval systems
based on this
technology.
Image retrieval matching can be performed using global features of the whole
image (as
in CBIR), but a comparison that considers part(s) of images for similarity-
based retrieval
is sometimes a more natural approach. Regions of an
image that are of particular interest
to humans are termed salient-objects of an
image. Salient –object
based image retrieval is
not well researched, but there some developed system that model the salient object of the
image using a Minimum Bounding Rectangle. In this work a new approach is proposed
which considers the natural shape of the salient objects contained within an
image. An
Image segmentation tool is used to automatically identify salient objects of an
image and
store them in an object relational DBMS for multi-criteria
image query (query
based on
both
image content, positional predicates and metadata). Furthermore, some techniques
have been proposed in this work to make the
image retrieval system accessible within a
pervasive computing environment.
Advisors/Committee Members: Dr. Solomon Atnafu (advisor).
Subjects/Keywords: Salient-Object Based Image Retrieval;
Image Database;
Image Retrieval in Pervasive Environment;
Image Segmentation;
Multi-Criteria Image Query
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Estifanos, G. (2008). Shape Sensitive Salient-object based Image Query
. (Thesis). Addis Ababa University. Retrieved from http://etd.aau.edu.et/dspace/handle/123456789/2702
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Estifanos, Gashaw. “Shape Sensitive Salient-object based Image Query
.” 2008. Thesis, Addis Ababa University. Accessed January 24, 2021.
http://etd.aau.edu.et/dspace/handle/123456789/2702.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Estifanos, Gashaw. “Shape Sensitive Salient-object based Image Query
.” 2008. Web. 24 Jan 2021.
Vancouver:
Estifanos G. Shape Sensitive Salient-object based Image Query
. [Internet] [Thesis]. Addis Ababa University; 2008. [cited 2021 Jan 24].
Available from: http://etd.aau.edu.et/dspace/handle/123456789/2702.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Estifanos G. Shape Sensitive Salient-object based Image Query
. [Thesis]. Addis Ababa University; 2008. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2702
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Anna University
15.
Grace Selvarani A.
Certain analysis on content based medical image retrieval
system using shape texture semantic and visual features.
Degree: 2013, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/11690
► Content-based image retrieval (CBIR) is a technique of searching the images based on the content of the image. CBIR is a promising technology to enrich…
(more)
▼ Content-based image retrieval (CBIR) is a technique
of searching the images based on the content of the image. CBIR is
a promising technology to enrich the core functionality of picture
archiving and communication systems (PACS) in medicine. CBIR in
medical domain has a potential for making a strong impact in
diagnostics, research and education. In this thesis, content based
medical image retrieval systems based on new techniques have been
proposed. In the conventional CBIR systems, the content of the
image is represented by features such as color, shape and texture
of the image. Various shape feature extraction methods are studied
and a new shape feature extraction method is proposed. Various
texture feature extraction methods are studied and a new texture
descriptor is proposed. A medical image retrieval system based on
this new texture descriptor is developed. The experimental results
show that this new framework outperforms the other existing texture
descriptor in medical CBIR systems. This research aims to
investigate an effective CBIR framework for combining the primary
image features. A multiple classifier framework for content based
image retrieval in medical domain has been proposed and implemented
by combining multiple shape and texture visual features. The deep
gap between visual features and high-level semantics is a major
obstacle to more effective image retrieval. To still improve the
retrieval performance of the medical CBIR system, a new hybrid
retrieval approach which combines visual shape, texture features
and high level semantics is proposed. The advantage of this
approach is that it supports both query by keyword and query by
image content. From the retrieval experiments conducted, it is
found that the proposed hybrid retrieval approach system has higher
retrieval performance than the proposed multiple classifier system
which uses only visual features. newline newline
newline
None
Advisors/Committee Members: Annadurai, S.
Subjects/Keywords: Content based image retrieval; Picture archiving and
communication systems; medical image retrieval system; texture;
semantic
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
A, G. S. (2013). Certain analysis on content based medical image retrieval
system using shape texture semantic and visual features. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/11690
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
A, Grace Selvarani. “Certain analysis on content based medical image retrieval
system using shape texture semantic and visual features.” 2013. Thesis, Anna University. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/11690.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
A, Grace Selvarani. “Certain analysis on content based medical image retrieval
system using shape texture semantic and visual features.” 2013. Web. 24 Jan 2021.
Vancouver:
A GS. Certain analysis on content based medical image retrieval
system using shape texture semantic and visual features. [Internet] [Thesis]. Anna University; 2013. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/11690.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
A GS. Certain analysis on content based medical image retrieval
system using shape texture semantic and visual features. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/11690
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Dalhousie University
16.
HE, DAAN.
Three new methods for color and texture based image matching
in Content-Based Image Retrieval.
Degree: PhD, Faculty of Computer Science, 2010, Dalhousie University
URL: http://hdl.handle.net/10222/12817
► Image matching is an important and necessary process in Content-Based Image Retrieval (CBIR). We propose three new methods for image matching: the first one is…
(more)
▼ Image matching is an important and necessary process
in Content-
Based Image Retrieval (CBIR). We propose three new
methods for
image matching: the first one is
based on the Local
Triplet Pattern (LTP) histograms; the second one is
based on the
Gaussian Mixture Models (GMMs) estimated by using the Extended
Mass-constraint (EMass) algorithm; and the third one is called the
DCT2KL algorithm. First, the LTP histograms are proposed to capture
spatial relationships between color levels of neighboring pixels.
An LTP level is extracted from each 3x3 pixel block, which is a
unique number describing the color level relationship between a
pixel and its neighboring pixels. Second, we consider how to
represent and compare multi-dimensional color features using GMMs.
GMMs are alternative methods to histograms for representing data
distributions. GMMs address the high-dimensional problems from
which histograms usually suffer inefficiency. In order to avoid
local maxima problems in most GMM estimation algorithms, we apply
the deterministic annealing method to estimate GMMs. Third,
motivated by
image compression algorithms, the DCT2KL method
addresses the high dimensional data by using the Discrete Cosine
Transform (DCT) coefficients in the YCbCr color space. The DCT
coefficients are restored by partially decoding JPEG images. Assume
that each DCT coefficient sequence is emitted from a memoryless
source, and all these sources are independent of each other. For
each target
image we form a hypothesis that its DCT coefficient
sequences are emitted from the same sources as the corresponding
sequences in the query
image. Testing these hypotheses by measuring
the log-likelihoods leads to a simple yet efficient scheme that
ranks each target
image according to the Kullback-Leibler (KL)
divergence between the empirical distribution of the DCT
coefficient sequences in the query
image and that in the target
image. Finally we present a scheme to combine different features
and methods to boost the performance of
image retrieval.
Experimental results on different
image data sets show that these
three methods proposed above outperform the related works in
literature, and the combination scheme further improves the
retrieval performance.
Advisors/Committee Members: Andrew K.C. Wong (external-examiner), Malcom Heywood (graduate-coordinator), Norm Scrimger (thesis-reader), Qigang Gao (thesis-reader), Christian Blouin (thesis-reader), Nick Cercone (thesis-supervisor), Not Applicable (ethics-approval), Not Applicable (manuscripts), Not Applicable (copyright-release).
Subjects/Keywords: Image matching; , Content-Based Image Retrieval; Color; Texture
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
HE, D. (2010). Three new methods for color and texture based image matching
in Content-Based Image Retrieval. (Doctoral Dissertation). Dalhousie University. Retrieved from http://hdl.handle.net/10222/12817
Chicago Manual of Style (16th Edition):
HE, DAAN. “Three new methods for color and texture based image matching
in Content-Based Image Retrieval.” 2010. Doctoral Dissertation, Dalhousie University. Accessed January 24, 2021.
http://hdl.handle.net/10222/12817.
MLA Handbook (7th Edition):
HE, DAAN. “Three new methods for color and texture based image matching
in Content-Based Image Retrieval.” 2010. Web. 24 Jan 2021.
Vancouver:
HE D. Three new methods for color and texture based image matching
in Content-Based Image Retrieval. [Internet] [Doctoral dissertation]. Dalhousie University; 2010. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/10222/12817.
Council of Science Editors:
HE D. Three new methods for color and texture based image matching
in Content-Based Image Retrieval. [Doctoral Dissertation]. Dalhousie University; 2010. Available from: http://hdl.handle.net/10222/12817
University of California – Berkeley
17.
Eden, Ashley Michelle.
Finding Lost Children.
Degree: Computer Science, 2010, University of California – Berkeley
URL: http://www.escholarship.org/uc/item/7sn371vm
► During a disaster, children may be quickly wrenched from their families. Research shows that children in such circumstances are often unable or unwilling to give…
(more)
▼ During a disaster, children may be quickly wrenched from their families. Research shows that children in such circumstances are often unable or unwilling to give their names or other identifying information. Currently in the US, there is no existing system in the public health infrastructure that exploits image-based analysis to effectively expedite reunification when children can't be identified. Working with the Children's Hospital Boston, we have engineered a system to speed reunification of children with their families, should they get separated in a disaster. Our system is based on a Content-Based Image Retrieval search using attributes and user feedback. In this thesis we will describe the system and a series of evaluations, including a realistic disaster drill set up and run jointly with the Children's Hospital.
Subjects/Keywords: Computer Science; Content-based image retrieval; mental image search; pediatric reunification
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Eden, A. M. (2010). Finding Lost Children. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/7sn371vm
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Eden, Ashley Michelle. “Finding Lost Children.” 2010. Thesis, University of California – Berkeley. Accessed January 24, 2021.
http://www.escholarship.org/uc/item/7sn371vm.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Eden, Ashley Michelle. “Finding Lost Children.” 2010. Web. 24 Jan 2021.
Vancouver:
Eden AM. Finding Lost Children. [Internet] [Thesis]. University of California – Berkeley; 2010. [cited 2021 Jan 24].
Available from: http://www.escholarship.org/uc/item/7sn371vm.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Eden AM. Finding Lost Children. [Thesis]. University of California – Berkeley; 2010. Available from: http://www.escholarship.org/uc/item/7sn371vm
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
18.
Kalra, Shivam.
Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words.
Degree: 2018, University of Waterloo
URL: http://hdl.handle.net/10012/13226
► The state-of-the-art image analysis algorithms offer a unique opportunity to extract semantically meaningful features from medical images. The advantage of this approach is automation in…
(more)
▼ The state-of-the-art image analysis algorithms offer a unique opportunity to extract semantically meaningful features from medical images. The advantage of this approach is automation in terms of content-based image retrieval (CBIR) of medical images. Such an automation leads to more reliable diagnostic decisions by clinicians as the direct beneficiary of these algorithms. Digital pathology (DP), or whole slide imaging (WSI), is a new avenue for image-based diagnosis in histopathology. WSI technology enables the digitization of traditional glass slides to ultra high-resolution digital images (or digital slides). Digital slides are more commonly used for CBIR research than other modalities of medical images due to their enormous size, increasing adoption among hospitals, and their various benefits offered to pathologists (e.g., digital telepathology). Pathology laboratories are under constant pressure to meet increasingly complex demands from hospitals. Many diseases (such as cancer) continue to grow which creates a pressing need to utilize existing innovative machine learning schemes to harness the knowledge contained in digital slides for more effective and efficient histopathology. This thesis provides a qualitative assessment of three popular image analysis techniques, namely Local Binary Pattern (LBP), Bag of visual Words (BoW), and Convolution Neural Networks (CNN) in their abilities to extract the discriminative features from gigapixel histopathology images. LBP and BoW are well-established techniques used in different image analysis problems. Over the last 5-10 years, CNN has become a frequent research topic in computer vision. CNN offers a domain-agnostic approach for the automatic extraction of discriminative image features, used for either classification or retrieval purposes. Therefore, it is imperative that this thesis gives more emphasis to CNN as a viable approach for the analysis of DP images. A new dataset, Kimia Path24 is specially designed and developed to facilitate the research in classification and CBIR of DP images. Kimia Path24 is used to measure the quality of image features extracted from LBP, BoW, and CNN; resulting in the best accuracy values of 41.33%, 54.67%, and 56.98% respectively. The results are somewhat surprising, suggesting that the handcrafted feature extraction algorithm, i.e., LBP can reach very close to the deep features extracted from CNN. It is unanticipated, considering that CNN requires much more computational resources and efforts for designing and fine-tuning. One of the conclusions is that CNN needs to be trained for the problem with a large number of training images to realize its comprehensive benefits. However, there are many situations where large, balanced, and the labeled dataset is not available; one such area is histopathology at present.
Subjects/Keywords: Content-based Image Retrieval; Digital Pathology; Deep Learning; Image Classification
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kalra, S. (2018). Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13226
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kalra, Shivam. “Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words.” 2018. Thesis, University of Waterloo. Accessed January 24, 2021.
http://hdl.handle.net/10012/13226.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kalra, Shivam. “Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words.” 2018. Web. 24 Jan 2021.
Vancouver:
Kalra S. Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/10012/13226.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kalra S. Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13226
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Ontario Institute of Technology
19.
Rizvi, Syed Raazi.
Comprehensive Investigation on Content-based Medical Image Retrieval using Radon Barcodes.
Degree: 2019, University of Ontario Institute of Technology
URL: http://hdl.handle.net/10155/1139
► Content Based Medical Image Retrieval (CBMIR) systems are vital to the underlying operation of medical databases because they allow quick search and retrieval of medical…
(more)
▼ Content
Based Medical
Image Retrieval (CBMIR) systems are vital to the underlying operation of medical databases because they allow quick search and
retrieval of medical images. Radon Barcode (RBC)s are binary complementary feature vectors which increase the speed of CBMIR systems through smaller feature vector size and low
retrieval error. We explore further improving the efficiency and accuracy of RBCs by optimizing the way they are extracted from medical images. Through the addition of
image pre-processing, novel barcoding techniques, and improved distance evaluation methods, we improved RBC utility in CBMIR applications.
Image pre-processing techniques such as histogram equalization and adaptive thresholding reduced the
retrieval error of generated RBCs. We also introduce several novel barcode generation techniques such as Binary Coded Decimal Radon Barcodes (BCDRBC), Difference of Radon Projections Barcodes (DRPBC), and Difference of Radon Projections Soft Hash Barcode (DRPSHBC) which decreased both
retrieval error and barcode size.
Advisors/Committee Members: Rahnamayan, Shahryar.
Subjects/Keywords: Content-based medical image retrieval; Radon barcodes; Image processing
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rizvi, S. R. (2019). Comprehensive Investigation on Content-based Medical Image Retrieval using Radon Barcodes. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/1139
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Rizvi, Syed Raazi. “Comprehensive Investigation on Content-based Medical Image Retrieval using Radon Barcodes.” 2019. Thesis, University of Ontario Institute of Technology. Accessed January 24, 2021.
http://hdl.handle.net/10155/1139.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Rizvi, Syed Raazi. “Comprehensive Investigation on Content-based Medical Image Retrieval using Radon Barcodes.” 2019. Web. 24 Jan 2021.
Vancouver:
Rizvi SR. Comprehensive Investigation on Content-based Medical Image Retrieval using Radon Barcodes. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/10155/1139.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Rizvi SR. Comprehensive Investigation on Content-based Medical Image Retrieval using Radon Barcodes. [Thesis]. University of Ontario Institute of Technology; 2019. Available from: http://hdl.handle.net/10155/1139
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
20.
Karmakar, Priyabrata.
Effective and efficient kernel-based image representations for classification and retrieval.
Degree: PhD, 2018, Federation University Australia
URL: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515
;
https://library.federation.edu.au/record=b2790194
► Image representation is a challenging task. In particular, in order to obtain better performances in different image processing applications such as video surveillance, autonomous driving,…
(more)
▼ Image representation is a challenging task. In particular, in order to obtain better performances in different image processing applications such as video surveillance, autonomous driving, crime scene detection and automatic inspection, effective and efficient image representation is a fundamental need. The performance of these applications usually depends on how accurately images are classified into their corresponding groups or how precisely relevant images are retrieved from a database based on a query. Accuracy in image classification and precision in image retrieval depend on the effectiveness of image representation. Existing image representation methods have some limitations. For example, spatial pyramid matching, which is a popular method incorporating spatial information in image-level representation, has not been fully studied to date. In addition, the strengths of pyramid match kernel and spatial pyramid matching are not combined for better image matching. Kernel descriptors based on gradient, colour and shape overcome the limitations of histogram-based descriptors, but suffer from information loss, noise effects and high computational complexity. Furthermore, the combined performance of kernel descriptors has limitations related to computational complexity, higher dimensionality and lower effectiveness. Moreover, the potential of a global texture descriptor which is based on human visual perception has not been fully explored to date. Therefore, in this research project, kernel-based effective and efficient image representation methods are proposed to address the above limitations. An enhancement is made to spatial pyramid matching in terms of improved rotation invariance. This is done by investigating different partitioning schemes suitable to achieve rotation-invariant image representation and the proposal of a weight function for appropriate level contribution in image matching. In addition, the strengths of pyramid match kernel and spatial pyramid are combined to enhance matching accuracy between images. The existing kernel descriptors are modified and improved to achieve greater effectiveness, minimum noise effects, less dimensionality and lower computational complexity. A novel fusion approach is also proposed to combine the information related to all pixel attributes, before the descriptor extraction stage. Existing kernel descriptors are based only on gradient, colour and shape information. In this research project, a texture-based kernel descriptor is proposed by modifying an existing popular global texture descriptor. Finally, all the contributions are evaluated in an integrated system. The performances of the proposed methods are qualitatively and quantitatively evaluated on two to four different publicly available image databases. The experimental results show that the proposed methods are more effective and efficient in image representation than existing benchmark methods.
Doctor of Philosophy
Subjects/Keywords: Image representation methods; Kernal-based image representations; Classification; Retrieval
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Karmakar, P. (2018). Effective and efficient kernel-based image representations for classification and retrieval. (Doctoral Dissertation). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194
Chicago Manual of Style (16th Edition):
Karmakar, Priyabrata. “Effective and efficient kernel-based image representations for classification and retrieval.” 2018. Doctoral Dissertation, Federation University Australia. Accessed January 24, 2021.
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194.
MLA Handbook (7th Edition):
Karmakar, Priyabrata. “Effective and efficient kernel-based image representations for classification and retrieval.” 2018. Web. 24 Jan 2021.
Vancouver:
Karmakar P. Effective and efficient kernel-based image representations for classification and retrieval. [Internet] [Doctoral dissertation]. Federation University Australia; 2018. [cited 2021 Jan 24].
Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194.
Council of Science Editors:
Karmakar P. Effective and efficient kernel-based image representations for classification and retrieval. [Doctoral Dissertation]. Federation University Australia; 2018. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194
University of Sydney
21.
Zhang, Fan.
Content-based Medical Image Classification and Retrieval
.
Degree: 2016, University of Sydney
URL: http://hdl.handle.net/2123/15531
► Content-based medical image classification and retrieval are the tasks finding medical images, e.g., of the same category of healthy or abnormal organs, for disease diagnosis…
(more)
▼ Content-based medical image classification and retrieval are the tasks finding medical images, e.g., of the same category of healthy or abnormal organs, for disease diagnosis and treatment in an automated way. They play important roles in establishing quantitative relationships among images by analyzing their content features. However, the features are often hindered by the inter- and intra-category visual variations between anatomical structures. Thus, it is important to design descriptive and discriminative feature descriptors. In this thesis, we develop novel fundamental content-based image feature analysis algorithms in medical image classification and retrieval in terms of different feature granularities: low-level feature, contextual descriptor, bag-of-visual-words (BoVW) representation and semantic association. In the interest of this study, we particularly focus on the following two applications: a lung nodule differentiation from low dose computed tomography images, and a neurodegenerative progression staging of Alzheimer's Disease (AD) given magnetic resonance and positron emission tomography scans. The thesis is divided into two parts: classification and retrieval. For the first part of this thesis, we focus on a classification task of lung nodule differentiation. It includes two lung nodule structure focus studies, which explore classification improvement by using new image information and identifying inter-category overlapping nodule images, and two contextual structure features, which investigate the combination of nodules and their surrounding anatomical structures. For the second part of this thesis, we work on two retrieval applications of finding intra-type lung nodule and intra-stage AD progression, with two feature designs of a pruned dictionary-based BoVW representation and a pair-wise latent association feature. The experimental evaluations demonstrated the better performance of the proposed methods.
Subjects/Keywords: Content-based Medical Image Analysis;
Medical Image Classification and Retrieval
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhang, F. (2016). Content-based Medical Image Classification and Retrieval
. (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/15531
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zhang, Fan. “Content-based Medical Image Classification and Retrieval
.” 2016. Thesis, University of Sydney. Accessed January 24, 2021.
http://hdl.handle.net/2123/15531.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhang, Fan. “Content-based Medical Image Classification and Retrieval
.” 2016. Web. 24 Jan 2021.
Vancouver:
Zhang F. Content-based Medical Image Classification and Retrieval
. [Internet] [Thesis]. University of Sydney; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2123/15531.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhang F. Content-based Medical Image Classification and Retrieval
. [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/15531
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Ohio University
22.
Hariharan, Sriram.
Image retrieval by spatial similarity a java - based
prototype.
Degree: MS, Electrical Engineering & Computer Science
(Engineering and Technology), 1998, Ohio University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1176496040
Subjects/Keywords: Java - Based Prototype; Image Retrieval; Content Based Image Retrieval
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hariharan, S. (1998). Image retrieval by spatial similarity a java - based
prototype. (Masters Thesis). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1176496040
Chicago Manual of Style (16th Edition):
Hariharan, Sriram. “Image retrieval by spatial similarity a java - based
prototype.” 1998. Masters Thesis, Ohio University. Accessed January 24, 2021.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1176496040.
MLA Handbook (7th Edition):
Hariharan, Sriram. “Image retrieval by spatial similarity a java - based
prototype.” 1998. Web. 24 Jan 2021.
Vancouver:
Hariharan S. Image retrieval by spatial similarity a java - based
prototype. [Internet] [Masters thesis]. Ohio University; 1998. [cited 2021 Jan 24].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1176496040.
Council of Science Editors:
Hariharan S. Image retrieval by spatial similarity a java - based
prototype. [Masters Thesis]. Ohio University; 1998. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1176496040
Queensland University of Technology
23.
Yuee, Liu.
Ontology-based image annotation.
Degree: 2010, Queensland University of Technology
URL: https://eprints.qut.edu.au/39611/
► With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted…
(more)
▼ With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by…
Subjects/Keywords: ontology; semantic extraction; image annotation; image segmentation; salient object; machine learning; content-based image retrieval
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Yuee, L. (2010). Ontology-based image annotation. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/39611/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Yuee, Liu. “Ontology-based image annotation.” 2010. Thesis, Queensland University of Technology. Accessed January 24, 2021.
https://eprints.qut.edu.au/39611/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Yuee, Liu. “Ontology-based image annotation.” 2010. Web. 24 Jan 2021.
Vancouver:
Yuee L. Ontology-based image annotation. [Internet] [Thesis]. Queensland University of Technology; 2010. [cited 2021 Jan 24].
Available from: https://eprints.qut.edu.au/39611/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Yuee L. Ontology-based image annotation. [Thesis]. Queensland University of Technology; 2010. Available from: https://eprints.qut.edu.au/39611/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Rochester Institute of Technology
24.
Davila Castellanos, Kenny.
Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment.
Degree: PhD, 2017, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9588
► Large data collections containing millions of math formulae in different formats are available on-line. Retrieving math expressions from these collections is challenging. We propose…
(more)
▼ Large data collections containing millions of math formulae in different formats are available on-line. Retrieving math expressions from these collections is challenging. We propose a framework for
retrieval of mathematical notation using symbol pairs extracted from visual and semantic representations of mathematical expressions on the symbolic domain for
retrieval of
text documents. We further adapt our model for
retrieval of mathematical notation on images and lecture videos. Graph-
based representations are used on each modality to describe math formulas. For symbolic formula
retrieval, where the structure is known, we use symbol layout trees and operator trees. For
image-
based formula
retrieval, since the structure is unknown we use a more general Line of Sight graph representation. Paths of these graphs define symbol pairs tuples that are used as the entries for our inverted index of mathematical notation. Our
retrieval framework uses a three-stage approach with a fast selection of candidates as the first layer, a more detailed matching algorithm with similarity metric computation in the second stage, and finally when relevance assessments are available, we use an optional third layer with linear regression for estimation of relevance using multiple similarity scores for final re-ranking. Our model has been evaluated using large collections of documents, and preliminary results are presented for videos and cross-modal search. The proposed framework can be adapted for other domains like chemistry or technical diagrams where two visually similar elements from a collection are usually related to each other.
Advisors/Committee Members: Richard Zanibbi.
Subjects/Keywords: Content based image retrieval; Information retrieval; Operator tree; Symbol layout tree; Symbol recognition; Video summarization
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Davila Castellanos, K. (2017). Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9588
Chicago Manual of Style (16th Edition):
Davila Castellanos, Kenny. “Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment.” 2017. Doctoral Dissertation, Rochester Institute of Technology. Accessed January 24, 2021.
https://scholarworks.rit.edu/theses/9588.
MLA Handbook (7th Edition):
Davila Castellanos, Kenny. “Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment.” 2017. Web. 24 Jan 2021.
Vancouver:
Davila Castellanos K. Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2017. [cited 2021 Jan 24].
Available from: https://scholarworks.rit.edu/theses/9588.
Council of Science Editors:
Davila Castellanos K. Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment. [Doctoral Dissertation]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9588
25.
Huang, Jia.
Recommendation with contextual information.
Degree: 2014, Drexel University
URL: http://hdl.handle.net/1860/4546
► Information retrieval (IR) systems have tremendously broaden users' access to information. However, users need to select their needs from trillions of information indexed daily. Due…
(more)
▼ Information retrieval (IR) systems have tremendously broaden users' access to information. However, users need to select their needs from trillions of information indexed daily. Due to the "semantic gap" between queries and indexed terms in IR system, whether users can satisfy their needs depends on whether they use the correct terms as queries. Recommender systems, have become a counterpart of information retrieval systems such as Google. They do not require users to specify their information needs in advance. They model users' preferences based on their history data, and automatically recommend items which satisfy their needs. In this way, both semantic gap and information overload can be alleviated. Collaborative filtering is the most popular recommendation algorithm used in academia and industry. However, it suffers from the cold start problem, where it cannot recommend new items to users or give recommendation to new users. Its recommendations also bias towards popular items.The goal of this thesis is to develop recommendation algorithms which can solve the item-side and user-side cold-start problems. We incorporate item content to solve the item-side cold start problem, and incorporate user similarity networks or social networks to solve the user-side cold-start problem. Our contributions are listed as follows. First, we develop a content-based movie recommendation algorithm which incorporates user, actor, and movie into the same model. Second, we develop a content-based music recommendation algorithm. The core component is a hierarchical dynamic programming algorithm to compute music similarities. The resulting music similarity matrix is then employed to make recommendations. Third, we apply social recommendation algorithms on Douban dataset. We look into a "Twitter-like" user-follower social network to see if it can improve recommendation performance. Finally, we validate the existence of social influence. We also analyze the social influence by investigating the correlation between one's influence and centrality, and by investigating how recommendation effectiveness changes with the number of recommendations.
Ph.D., Information Studies – Drexel University, 2014
Advisors/Committee Members: Hu, Xiaohua, College of Computing and Informatics.
Subjects/Keywords: Information studies; Information storage and retrieval systems; Content-based image retrieval; Recommender systems (Information filtering)
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Huang, J. (2014). Recommendation with contextual information. (Thesis). Drexel University. Retrieved from http://hdl.handle.net/1860/4546
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Huang, Jia. “Recommendation with contextual information.” 2014. Thesis, Drexel University. Accessed January 24, 2021.
http://hdl.handle.net/1860/4546.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Huang, Jia. “Recommendation with contextual information.” 2014. Web. 24 Jan 2021.
Vancouver:
Huang J. Recommendation with contextual information. [Internet] [Thesis]. Drexel University; 2014. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/1860/4546.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Huang J. Recommendation with contextual information. [Thesis]. Drexel University; 2014. Available from: http://hdl.handle.net/1860/4546
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
26.
Larsson, Jimmy.
Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources.
Degree: Computer Science and Communication (CSC), 2016, KTH
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574
► With a multitude of images available on the Internet, how do we find what we are looking for? This project tries to determine how…
(more)
▼ With a multitude of images available on the Internet, how do we find what we are looking for? This project tries to determine how much the precision and recall of search queries is improved by using a word taxonomy on traditional Text-Based Image Search and Content-Based Image Search. By applying a word taxonomy to different data sources, a strong keyword filter and a keyword extender were implemented and tested. The results show that depending on the implementation, the precision or the recall can be increased. By using a similar approach on real life implementations, it is possible to force images with higher precisions to the front while keeping a high recall value, thus increasing the experienced relevance of image search.
Med den mängd bilder som nu finns tillgänglig på Internet, hur kan vi fortfarande hitta det vi letar efter? Denna uppsats försöker avgöra hur mycket bildprecision och bildåterkallning kan öka med hjälp av appliceringen av en ordtaxonomi på traditionell Text-Based Image Search och Content-Based Image Search. Genom att applicera en ordtaxonomi på olika datakällor kan ett starkt ordfilter samt en modul som förlänger ordlistor skapas och testas. Resultaten pekar på att beroende på implementationen så kan antingen precisionen eller återkallningen förbättras. Genom att använda en liknande metod i ett verkligt scenario är det därför möjligt att flytta bilder med hög precision längre fram i resultatlistan och samtidigt behålla hög återkallning, och därmed öka den upplevda relevansen i bildsök.
Subjects/Keywords: Taxonomy; Image Retrieval; TBIR; CBIR; WordNet; Text-Based Image Search; Content-Based Image Search; Computer Sciences; Datavetenskap (datalogi)
…Forest . . . . . . . . . . . . . . . . . . . . . . . . . .
4 Text-Based Image Retrieval
4.1… …6.4 Text-Based Image Retrieval . . . . . . . .
6.4.1 Natural Language Processing . . .
6.4.2… …call Text-Based Image Retrieval (TBIR), also known as Context-Based Image Retrieval… …and compare that to already known data. The Text-Based
Image Retrieval of today on the other… …some degree, what the image
may portray. While Content-Based and Text-Based Image Retrieval…
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Larsson, J. (2016). Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Larsson, Jimmy. “Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources.” 2016. Thesis, KTH. Accessed January 24, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Larsson, Jimmy. “Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources.” 2016. Web. 24 Jan 2021.
Vancouver:
Larsson J. Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources. [Internet] [Thesis]. KTH; 2016. [cited 2021 Jan 24].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Larsson J. Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Alberta
27.
Hajebi, Kiana.
Efficient Visual Search in Appearance-based SLAM.
Degree: PhD, Department of Computing Science, 2015, University of Alberta
URL: https://era.library.ualberta.ca/files/k643b385g
► Simultaneous localization and mapping (SLAM) in an unknown environment is a prerequisite to have a truly autonomous mobile robot. In this thesis, we focus on…
(more)
▼ Simultaneous localization and mapping (SLAM) in an
unknown environment is a prerequisite to have a truly autonomous
mobile robot. In this thesis, we focus on appearance-based visual
SLAM, for which we develop a graph-based nearest-neighbor search
algorithm to speed up bag-of-words (BoW) image retrieval. In
appearance-based SLAM, the robot uses the visual appearance of the
locations taken along its route to build the map of the environment
and localizes itself within this map by recognizing places it has
visited before. To solve the challenging problem of
appearance-based place recognition (a.k.a. loop closure detection
in the context of SLAM) in large-scale environments, we employ the
bag-of-words model, because of its efficiency in image
representation and retrieval. Moreover, because the complexity of
BoW does not grow with the size of the dataset, as much as that of
other search techniques (e.g. direct feature matching) do, it can
be employed for large-scale image search applications. Although BoW
provides an efficient search technique, its vector-quantization
(VQ) step can be computationally expensive in order to provide
sufficient discriminating power for image matching. In order to
speed up the VQ process, we propose a graph-based nearest neighbor
search method (GNNS) that builds a k-NN graph index, and is
efficiently integrated into the SLAM system. The k-NN graph is
constructed over the visual words of the vocabulary. At search
time, the standard GNNS algorithm starts from a randomly sampled
node in the graph and performs hill-climbing until reaching a local
minimum which is then an approximate nearest neighbor of the
queried feature. We modify the GNNS algorithm to be initialized
judiciously by exploiting the following properties of SLAM and BoW
model: (1) the sequential property of images acquired in
appearance-based SLAM, (2) the perceptual aliasing problem inherent
to BoW. This smart initialization of GNNS improves the speedup of
the vector-quantization considerably. We experimentally show that
our search method outperforms the popular KD-trees and hierarchical
k-means algorithms. Furthermore, we develop a SLAM system by
integrating the BoW’s loop closure detection in a Bayesian
filtering framework. This probabilistic framework then discards
false loop closures by enforcing the temporal coherency of
estimation. We run our SLAM system on different datasets to test
our loop closure detection in challenging environments. The system
successfully detects loop closures with 100% precision and high
recall rates in real-time.
Subjects/Keywords: Graph Nearest Neighbor Search; Image Retrieval; Robotics; Appearance-based SLAM
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hajebi, K. (2015). Efficient Visual Search in Appearance-based SLAM. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/k643b385g
Chicago Manual of Style (16th Edition):
Hajebi, Kiana. “Efficient Visual Search in Appearance-based SLAM.” 2015. Doctoral Dissertation, University of Alberta. Accessed January 24, 2021.
https://era.library.ualberta.ca/files/k643b385g.
MLA Handbook (7th Edition):
Hajebi, Kiana. “Efficient Visual Search in Appearance-based SLAM.” 2015. Web. 24 Jan 2021.
Vancouver:
Hajebi K. Efficient Visual Search in Appearance-based SLAM. [Internet] [Doctoral dissertation]. University of Alberta; 2015. [cited 2021 Jan 24].
Available from: https://era.library.ualberta.ca/files/k643b385g.
Council of Science Editors:
Hajebi K. Efficient Visual Search in Appearance-based SLAM. [Doctoral Dissertation]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/k643b385g
Anna University
28.
Renukadevi N T.
Certain improvements in support Vector machine kernel
using hybrid Evolutionary algorithms for Medical image
classification;.
Degree: Certain improvements in support Vector machine kernel
using hybrid Evolutionary algorithms for Medical image
classification, 2015, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/49416
► With the technological advances in digital imaging large newlinecollections of medical images are generated and stored in the medical newlinedatabases X ray CT MRI PET…
(more)
▼ With the technological advances in digital imaging
large newlinecollections of medical images are generated and stored
in the medical newlinedatabases X ray CT MRI PET ultrasound images
are a major source of newlineanatomical and functional information
needed for diagnosis research and newlineteaching With the amount
of digital medical images generated the medical newlinedatabase is
huge and multi varied The need to search data collections
newlineefficiently requires good image retrieval systems Content
Based Image newlineRetrieval CBIR helps to retrieve images from the
database similar to the newlinequery image are now widely used for
medical image retrieval CBIR is newlinebasically a two step process
such as Feature Extraction and Feature Matching newlineFeature
Extraction is the process to extract image features to a
distinguishable newlineExtend Information extracted from images
such as colour texture and shape newlineare known as feature
vectors This process is done on both query image and newlineimages
in databases Feature Matching involves comparing the features of
newlineboth the images in the database newlineIncorporating
adaptable techniques to process the images of varied
newlinecharacteristics and classes is the biggest issue in CBIR
systems In this newlineresearch the problem of retrieving medical
images from a multi varied newlinedatabase is addressed The
underlying objective of this research work is to newlineinvestigate
the efficacy of the various classification algorithms used in
content newlinebased image retrieval and proposes a novel
classification algorithm newline newline
reference p119-130.
Advisors/Committee Members: Thangaraj P.
Subjects/Keywords: Content Based Image Retrieval; Medical databases X ray
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
T, R. N. (2015). Certain improvements in support Vector machine kernel
using hybrid Evolutionary algorithms for Medical image
classification;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/49416
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
T, Renukadevi N. “Certain improvements in support Vector machine kernel
using hybrid Evolutionary algorithms for Medical image
classification;.” 2015. Thesis, Anna University. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/49416.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
T, Renukadevi N. “Certain improvements in support Vector machine kernel
using hybrid Evolutionary algorithms for Medical image
classification;.” 2015. Web. 24 Jan 2021.
Vancouver:
T RN. Certain improvements in support Vector machine kernel
using hybrid Evolutionary algorithms for Medical image
classification;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/49416.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
T RN. Certain improvements in support Vector machine kernel
using hybrid Evolutionary algorithms for Medical image
classification;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/49416
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
29.
Raghuraman G.
Content based image retrieval in Medical image
databases;.
Degree: Content based image retrieval in Medical image
databases, 2015, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/40114
► With the availability of easy and inexpensive methods to create and newlinestore images in digital formats the visual information preserved and shared newlineelectronically has grown…
(more)
▼ With the availability of easy and inexpensive
methods to create and newlinestore images in digital formats the
visual information preserved and shared newlineelectronically has
grown dramatically Efficient image searching, browsing newlineand
retrieval tools are required by users from various domains
including newlineremote sensing, crime prevention publishing
medical forensic etc For this newlinepurpose many general purpose
image retrieval systems have been developed newlineText based image
retrieval are based on language but variations in annotation
newlinewill pose challenges to image retrieval Content based image
retrieval relies newlineon the characterization of primitive
features such as color shape and texture newlinethat can be
automatically extracted from the images themselves The main
newlineargument leveled against these Content Based Image Retrieval
systems newlineconcerns their lack of robustness to rotation and
occlusion newlineAlso most of the conventional image retrieval
systems lack the newlinecapability to utilize human intuition and
emotion appropriately in the process newlineof image retrieval It
is difficult to retrieve a satisfactory result when the user
newlinewants an image that cannot be explicitly specified The
difference between the newlineusers information and the image
representation is called the semantic gap in newlineContent Based
Image Retrieval systems The limited retrieval accuracy of
newlineimage centric retrieval systems is basically due to the
inherent semantic gap newline newline
reference p146-160p.
Advisors/Committee Members: Shunmuganathan K L.
Subjects/Keywords: Content Based Image Retrieval systems; Inherent semantic gap
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
G, R. (2015). Content based image retrieval in Medical image
databases;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/40114
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
G, Raghuraman. “Content based image retrieval in Medical image
databases;.” 2015. Thesis, Anna University. Accessed January 24, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/40114.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
G, Raghuraman. “Content based image retrieval in Medical image
databases;.” 2015. Web. 24 Jan 2021.
Vancouver:
G R. Content based image retrieval in Medical image
databases;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Jan 24].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40114.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
G R. Content based image retrieval in Medical image
databases;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40114
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Waterloo
30.
Sriram, Aditya.
Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images.
Degree: 2018, University of Waterloo
URL: http://hdl.handle.net/10012/13165
► Clinical analysis and medical diagnosis of diverse diseases adopt medical imaging techniques to empower specialists to perform their tasks by visualizing internal body organs and…
(more)
▼ Clinical analysis and medical diagnosis of diverse diseases adopt medical imaging techniques to empower specialists to perform their tasks by visualizing internal body organs and tissues for classifying and treating diseases at an early stage. Content-Based Image Retrieval (CBIR) systems are a set of computer vision techniques to retrieve similar images from a large database based on proper image representations. Particularly in radiology and histopathology, CBIR is a promising approach to effectively screen, understand, and retrieve images with similar level of semantic descriptions from a database of previously diagnosed cases to provide physicians with reliable assistance for diagnosis, treatment planning and research.
Over the past decade, the development of CBIR systems in medical imaging has expedited due to the increase in digitized modalities, an increase in computational efficiency (e.g., availability of GPUs), and progress in algorithm development in computer vision and artificial intelligence. Hence, medical specialists may use CBIR prototypes to query similar cases from a large image database based solely on the image content (and no text). Understanding the semantics of an image requires an expressive descriptor that has the ability to capture and to represent unique and invariant features of an image. Radon transform, one of the oldest techniques widely used in medical imaging, can capture the shape of organs in form of a one-dimensional histogram by projecting parallel rays through a two-dimensional object of concern at a specific angle. In this work, the Radon transform is re-designed to (i) extract features and (ii) generate a descriptor for content-based retrieval of medical images. Radon transform is applied to feed a deep neural network instead of raw images in order to improve the generalization of the network. Specifically, the framework is composed of providing Radon projections of an image to a deep autoencoder, from which the deepest layer is isolated and fed into a multi-layer perceptron for classification. This approach enables the network to (a) train much faster as the Radon projections are computationally inexpensive compared to raw input images, and (b) perform more accurately as Radon projections can make more pronounced and salient features to the network compared to raw images. This framework is validated on a publicly available radiography data set called "Image Retrieval in Medical Applications" (IRMA), consisting of 12,677 train and 1,733 test images, for which an classification accuracy of approximately 82% is achieved, outperforming all autoencoder strategies reported on the Image Retrieval in Medical Applications (IRMA) dataset. The classification accuracy is calculated by dividing the total IRMA error, a calculation outlined by the authors of the data set, with the total number of test images. Finally, a compact handcrafted image descriptor based on Radon transform was designed in this work that is called "Forming Local Intersections of Projections" (FLIP).
The FLIP…
Subjects/Keywords: Machine Learning; Medical Imaging; Radon transform; Content-based Image Retrieval
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sriram, A. (2018). Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13165
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sriram, Aditya. “Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images.” 2018. Thesis, University of Waterloo. Accessed January 24, 2021.
http://hdl.handle.net/10012/13165.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sriram, Aditya. “Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images.” 2018. Web. 24 Jan 2021.
Vancouver:
Sriram A. Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/10012/13165.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sriram A. Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13165
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
◁ [1] [2] [3] [4] [5] … [1806] ▶
. 
Open Access Theses and Dissertations
