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You searched for subject:(Locality sensitive hashing). Showing records 1 – 27 of 27 total matches.

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1. Moran, Sean James. Learning to hash for large scale image retrieval.

Degree: PhD, 2016, University of Edinburgh

 This thesis is concerned with improving the effectiveness of nearest neighbour search. Nearest neighbour search is the problem of finding the most similar data-points to… (more)

Subjects/Keywords: 006.3; Locality Sensitive Hashing; LSH; image retrieval

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APA (6th Edition):

Moran, S. J. (2016). Learning to hash for large scale image retrieval. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/20390

Chicago Manual of Style (16th Edition):

Moran, Sean James. “Learning to hash for large scale image retrieval.” 2016. Doctoral Dissertation, University of Edinburgh. Accessed April 26, 2019. http://hdl.handle.net/1842/20390.

MLA Handbook (7th Edition):

Moran, Sean James. “Learning to hash for large scale image retrieval.” 2016. Web. 26 Apr 2019.

Vancouver:

Moran SJ. Learning to hash for large scale image retrieval. [Internet] [Doctoral dissertation]. University of Edinburgh; 2016. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1842/20390.

Council of Science Editors:

Moran SJ. Learning to hash for large scale image retrieval. [Doctoral Dissertation]. University of Edinburgh; 2016. Available from: http://hdl.handle.net/1842/20390


McMaster University

2. Lu, Yangdi. Salient Index for Similarity Search Over High Dimensional Vectors.

Degree: MSc, 2018, McMaster University

The approximate nearest neighbor(ANN) search over high dimensional data has become an unavoidable service for online applications. Fast and high-quality results of unknown queries are… (more)

Subjects/Keywords: approximate nearest neighbor; locality sensitive hashing; index structure; searching strategy

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APA (6th Edition):

Lu, Y. (2018). Salient Index for Similarity Search Over High Dimensional Vectors. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/23970

Chicago Manual of Style (16th Edition):

Lu, Yangdi. “Salient Index for Similarity Search Over High Dimensional Vectors.” 2018. Masters Thesis, McMaster University. Accessed April 26, 2019. http://hdl.handle.net/11375/23970.

MLA Handbook (7th Edition):

Lu, Yangdi. “Salient Index for Similarity Search Over High Dimensional Vectors.” 2018. Web. 26 Apr 2019.

Vancouver:

Lu Y. Salient Index for Similarity Search Over High Dimensional Vectors. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/11375/23970.

Council of Science Editors:

Lu Y. Salient Index for Similarity Search Over High Dimensional Vectors. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/23970


University of Alberta

3. Shahbazi, Hossein. Application of locality sensitive hashing to feature matching and loop closure detection.

Degree: MS, Department of Computing Science, 2011, University of Alberta

 My thesis focuses on automatic parameter selection for euclidean distance version of Locality Sensitive Hashing (LSH) and solving visual loop closure detection by using LSH.… (more)

Subjects/Keywords: locality sensitive hashing; nearest neighbor search; parameter selection; visual slam; loop closure detection

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APA (6th Edition):

Shahbazi, H. (2011). Application of locality sensitive hashing to feature matching and loop closure detection. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cc08hg50x

Chicago Manual of Style (16th Edition):

Shahbazi, Hossein. “Application of locality sensitive hashing to feature matching and loop closure detection.” 2011. Masters Thesis, University of Alberta. Accessed April 26, 2019. https://era.library.ualberta.ca/files/cc08hg50x.

MLA Handbook (7th Edition):

Shahbazi, Hossein. “Application of locality sensitive hashing to feature matching and loop closure detection.” 2011. Web. 26 Apr 2019.

Vancouver:

Shahbazi H. Application of locality sensitive hashing to feature matching and loop closure detection. [Internet] [Masters thesis]. University of Alberta; 2011. [cited 2019 Apr 26]. Available from: https://era.library.ualberta.ca/files/cc08hg50x.

Council of Science Editors:

Shahbazi H. Application of locality sensitive hashing to feature matching and loop closure detection. [Masters Thesis]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/cc08hg50x


University of Southern California

4. Behnam, Ehsan. Geometric interpretation of biological data: algorithmic solutions for next generation sequencing analysis at massive scale.

Degree: PhD, Computational Biology and Bioinformatics, 2015, University of Southern California

 Recent advances in high-throughput sequencing technologies have provided scientists with unprecedented abilities to unravel the secrets of nature. Substantial cost reduction in such technologies has… (more)

Subjects/Keywords: next generation sequencing; alignment-free sequence comparison; data structures; locality sensitive hashing

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APA (6th Edition):

Behnam, E. (2015). Geometric interpretation of biological data: algorithmic solutions for next generation sequencing analysis at massive scale. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/541361/rec/3028

Chicago Manual of Style (16th Edition):

Behnam, Ehsan. “Geometric interpretation of biological data: algorithmic solutions for next generation sequencing analysis at massive scale.” 2015. Doctoral Dissertation, University of Southern California. Accessed April 26, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/541361/rec/3028.

MLA Handbook (7th Edition):

Behnam, Ehsan. “Geometric interpretation of biological data: algorithmic solutions for next generation sequencing analysis at massive scale.” 2015. Web. 26 Apr 2019.

Vancouver:

Behnam E. Geometric interpretation of biological data: algorithmic solutions for next generation sequencing analysis at massive scale. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2019 Apr 26]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/541361/rec/3028.

Council of Science Editors:

Behnam E. Geometric interpretation of biological data: algorithmic solutions for next generation sequencing analysis at massive scale. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/541361/rec/3028


University of Texas – Austin

5. Jain, Prateek. Large scale optimization methods for metric and kernel learning.

Degree: Computer Sciences, 2009, University of Texas – Austin

 A large number of machine learning algorithms are critically dependent on the underlying distance/metric/similarity function. Learning an appropriate distance function is therefore crucial to the… (more)

Subjects/Keywords: Rank minimization; Metric learning; Kernel learning; Fast similarity search; Locality sensitive hashing

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APA (6th Edition):

Jain, P. (2009). Large scale optimization methods for metric and kernel learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/27132

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):

Jain, Prateek. “Large scale optimization methods for metric and kernel learning.” 2009. Thesis, University of Texas – Austin. Accessed April 26, 2019. http://hdl.handle.net/2152/27132.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jain, Prateek. “Large scale optimization methods for metric and kernel learning.” 2009. Web. 26 Apr 2019.

Vancouver:

Jain P. Large scale optimization methods for metric and kernel learning. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2152/27132.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jain P. Large scale optimization methods for metric and kernel learning. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/27132

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of New South Wales

6. Sunarso, Freddie. Protein Sequence Similarity Search using Locality-Sensitive Hashing and MapReduce.

Degree: Computer Science & Engineering, 2014, University of New South Wales

 Metagenomic studies produce large datasets that are estimated to grow at a faster rate than the available computational capacity. A key step in the metagenomic… (more)

Subjects/Keywords: Protein Sequence Similarity Search; Locality-Sensitive Hashing; MapReduce; Big Data; Cloud Computing; Metagenomics; ScalLoPS

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APA (6th Edition):

Sunarso, F. (2014). Protein Sequence Similarity Search using Locality-Sensitive Hashing and MapReduce. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53681 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12376/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Sunarso, Freddie. “Protein Sequence Similarity Search using Locality-Sensitive Hashing and MapReduce.” 2014. Masters Thesis, University of New South Wales. Accessed April 26, 2019. http://handle.unsw.edu.au/1959.4/53681 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12376/SOURCE02?view=true.

MLA Handbook (7th Edition):

Sunarso, Freddie. “Protein Sequence Similarity Search using Locality-Sensitive Hashing and MapReduce.” 2014. Web. 26 Apr 2019.

Vancouver:

Sunarso F. Protein Sequence Similarity Search using Locality-Sensitive Hashing and MapReduce. [Internet] [Masters thesis]. University of New South Wales; 2014. [cited 2019 Apr 26]. Available from: http://handle.unsw.edu.au/1959.4/53681 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12376/SOURCE02?view=true.

Council of Science Editors:

Sunarso F. Protein Sequence Similarity Search using Locality-Sensitive Hashing and MapReduce. [Masters Thesis]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53681 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12376/SOURCE02?view=true

7. YANG ZIXIANG. Efficient video identification based on locality sensitive hashing and triangle inequality.

Degree: 2005, National University of Singapore

Subjects/Keywords: video identification; video search; video hashing; locality sensitive hashing

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APA (6th Edition):

ZIXIANG, Y. (2005). Efficient video identification based on locality sensitive hashing and triangle inequality. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/15115 ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/2/bitstream ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/1/bitstream

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):

ZIXIANG, YANG. “Efficient video identification based on locality sensitive hashing and triangle inequality.” 2005. Thesis, National University of Singapore. Accessed April 26, 2019. http://scholarbank.nus.edu.sg/handle/10635/15115 ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/2/bitstream ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/1/bitstream.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

ZIXIANG, YANG. “Efficient video identification based on locality sensitive hashing and triangle inequality.” 2005. Web. 26 Apr 2019.

Vancouver:

ZIXIANG Y. Efficient video identification based on locality sensitive hashing and triangle inequality. [Internet] [Thesis]. National University of Singapore; 2005. [cited 2019 Apr 26]. Available from: http://scholarbank.nus.edu.sg/handle/10635/15115 ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/2/bitstream ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/1/bitstream.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

ZIXIANG Y. Efficient video identification based on locality sensitive hashing and triangle inequality. [Thesis]. National University of Singapore; 2005. Available from: http://scholarbank.nus.edu.sg/handle/10635/15115 ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/2/bitstream ; http://scholarbank.nus.edu.sg/bitstream/10635%2F15115/1/bitstream

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


EPFL

8. Haghani, Parisa. Efficient Processing of Ranking Queries in Novel Applications.

Degree: 2010, EPFL

 Ranking queries, which return only a subset of results matching a user query, have been studied extensively in the past decade due to their importance… (more)

Subjects/Keywords: ranking queries; top-k; nearest neighbor; data streams; P2P; load shedding; query indexing; filtering; locality sensitive hashing

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APA (6th Edition):

Haghani, P. (2010). Efficient Processing of Ranking Queries in Novel Applications. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/149391

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):

Haghani, Parisa. “Efficient Processing of Ranking Queries in Novel Applications.” 2010. Thesis, EPFL. Accessed April 26, 2019. http://infoscience.epfl.ch/record/149391.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Haghani, Parisa. “Efficient Processing of Ranking Queries in Novel Applications.” 2010. Web. 26 Apr 2019.

Vancouver:

Haghani P. Efficient Processing of Ranking Queries in Novel Applications. [Internet] [Thesis]. EPFL; 2010. [cited 2019 Apr 26]. Available from: http://infoscience.epfl.ch/record/149391.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Haghani P. Efficient Processing of Ranking Queries in Novel Applications. [Thesis]. EPFL; 2010. Available from: http://infoscience.epfl.ch/record/149391

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cincinnati

9. Carraher, Lee A. Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data.

Degree: PhD, Engineering and Applied Science: Computer Science and Engineering, 2018, University of Cincinnati

 Clustering data has gained popularity in recent years due to an expanding opportunity to discover knowledgeand collect insights from multiple widely available and diverse data… (more)

Subjects/Keywords: Computer Engineering; data clustering; distributed data mining; streaming data algorithms; locality sensitive hashing; count-min cut tree; random projection

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APA (6th Edition):

Carraher, L. A. (2018). Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511860805777818

Chicago Manual of Style (16th Edition):

Carraher, Lee A. “Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data.” 2018. Doctoral Dissertation, University of Cincinnati. Accessed April 26, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511860805777818.

MLA Handbook (7th Edition):

Carraher, Lee A. “Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data.” 2018. Web. 26 Apr 2019.

Vancouver:

Carraher LA. Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data. [Internet] [Doctoral dissertation]. University of Cincinnati; 2018. [cited 2019 Apr 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511860805777818.

Council of Science Editors:

Carraher LA. Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data. [Doctoral Dissertation]. University of Cincinnati; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511860805777818


Pontifical Catholic University of Rio de Janeiro

10. PEDRO NUNO DE SOUZA MOURA. [en] LSHSIM: A LOCALITY SENSITIVE HASHING BASED METHOD FOR MULTIPLE-POINT GEOSTATISTICS.

Degree: 2017, Pontifical Catholic University of Rio de Janeiro

[pt] A modelagem de reservatórios consiste em uma tarefa de muita relevância na medida em que permite a representação de uma dada região geológica de… (more)

Subjects/Keywords: [pt] GEOESTATISTICA MULTIPONTO; [en] MULTIPLE-POINT GEOSTATISTICS; [pt] LOCALITY SENSITIVE HASHING; [en] LOCALITY SENSITIVE HASHING; [pt] RUN-LENGTH ENCODING; [en] RUN-LENGTH ENCODING; [pt] MODELAGEM DE PADROES; [en] PATTERN MODELING; [pt] IMAGEM DE TREINAMENTO; [en] TRAINING IMAGE

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APA (6th Edition):

MOURA, P. N. D. S. (2017). [en] LSHSIM: A LOCALITY SENSITIVE HASHING BASED METHOD FOR MULTIPLE-POINT GEOSTATISTICS. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=32005

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):

MOURA, PEDRO NUNO DE SOUZA. “[en] LSHSIM: A LOCALITY SENSITIVE HASHING BASED METHOD FOR MULTIPLE-POINT GEOSTATISTICS.” 2017. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed April 26, 2019. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=32005.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

MOURA, PEDRO NUNO DE SOUZA. “[en] LSHSIM: A LOCALITY SENSITIVE HASHING BASED METHOD FOR MULTIPLE-POINT GEOSTATISTICS.” 2017. Web. 26 Apr 2019.

Vancouver:

MOURA PNDS. [en] LSHSIM: A LOCALITY SENSITIVE HASHING BASED METHOD FOR MULTIPLE-POINT GEOSTATISTICS. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2017. [cited 2019 Apr 26]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=32005.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

MOURA PNDS. [en] LSHSIM: A LOCALITY SENSITIVE HASHING BASED METHOD FOR MULTIPLE-POINT GEOSTATISTICS. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2017. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=32005

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Indian Institute of Science

11. Ranganath, B N. Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification.

Degree: 2008, Indian Institute of Science

 Data mining is an area to find valid, novel, potentially useful, and ultimately understandable abstractions in a data. Frequent itemset mining is one of the… (more)

Subjects/Keywords: Data Mining; Classification - Algorithms; Frequent Itemset Mining; Clustered Itemsets; Data Stream Mining; Locality Sensitive Hashing; Stream-Close Algorithm; Associative Classification; Clustered Frequent Itemsets; Closed Frequent Itemsets; Stream Mining; Computer Science

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APA (6th Edition):

Ranganath, B. N. (2008). Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/830

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):

Ranganath, B N. “Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification.” 2008. Thesis, Indian Institute of Science. Accessed April 26, 2019. http://hdl.handle.net/2005/830.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ranganath, B N. “Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification.” 2008. Web. 26 Apr 2019.

Vancouver:

Ranganath BN. Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification. [Internet] [Thesis]. Indian Institute of Science; 2008. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2005/830.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ranganath BN. Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification. [Thesis]. Indian Institute of Science; 2008. Available from: http://hdl.handle.net/2005/830

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Pretoria

12. Van Wyk, Frans Pieter. Simultaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments.

Degree: Electrical, Electronic and Computer Engineering, 2013, University of Pretoria

 Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into… (more)

Subjects/Keywords: Object recognition; Pose estimation; Real-time; Partial object matching; 3D features; Free form deformation; Data compression; Locality sensitive hashing; Structured light; Intelligent systems; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Van Wyk, F. P. (2013). Simultaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/41506

Chicago Manual of Style (16th Edition):

Van Wyk, Frans Pieter. “Simultaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments.” 2013. Masters Thesis, University of Pretoria. Accessed April 26, 2019. http://hdl.handle.net/2263/41506.

MLA Handbook (7th Edition):

Van Wyk, Frans Pieter. “Simultaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments.” 2013. Web. 26 Apr 2019.

Vancouver:

Van Wyk FP. Simultaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments. [Internet] [Masters thesis]. University of Pretoria; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2263/41506.

Council of Science Editors:

Van Wyk FP. Simultaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments. [Masters Thesis]. University of Pretoria; 2013. Available from: http://hdl.handle.net/2263/41506


Arizona State University

13. Bhat, Aneesha. Locality Sensitive Indexing for Efficient High-Dimensional Query Answering in the Presence of Excluded Regions.

Degree: Computer Science, 2016, Arizona State University

Subjects/Keywords: Computer science; High-Dimensional; Locality Sensitive Hashing; Negative Queries; Range Search; Similarity Search

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bhat, A. (2016). Locality Sensitive Indexing for Efficient High-Dimensional Query Answering in the Presence of Excluded Regions. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/36534

Chicago Manual of Style (16th Edition):

Bhat, Aneesha. “Locality Sensitive Indexing for Efficient High-Dimensional Query Answering in the Presence of Excluded Regions.” 2016. Masters Thesis, Arizona State University. Accessed April 26, 2019. http://repository.asu.edu/items/36534.

MLA Handbook (7th Edition):

Bhat, Aneesha. “Locality Sensitive Indexing for Efficient High-Dimensional Query Answering in the Presence of Excluded Regions.” 2016. Web. 26 Apr 2019.

Vancouver:

Bhat A. Locality Sensitive Indexing for Efficient High-Dimensional Query Answering in the Presence of Excluded Regions. [Internet] [Masters thesis]. Arizona State University; 2016. [cited 2019 Apr 26]. Available from: http://repository.asu.edu/items/36534.

Council of Science Editors:

Bhat A. Locality Sensitive Indexing for Efficient High-Dimensional Query Answering in the Presence of Excluded Regions. [Masters Thesis]. Arizona State University; 2016. Available from: http://repository.asu.edu/items/36534


University of Victoria

14. Faghfouri, Aidin. Distributed high-dimensional similarity search with music information retrieval applications.

Degree: Dept. of Computer Science, 2011, University of Victoria

 Today, the advent of networking technologies and computer hardware have enabled more and more inexpensive PCs, various mobile devices, smart phones, PDAs, sensors and cameras… (more)

Subjects/Keywords: similarity search; music; peer-to-peer; distributed similarity search; music information retrieval; locality sensitive hashing; music similarity search; high-dimensional similarity search; marsyas; P2P; feature vector; music features

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Faghfouri, A. (2011). Distributed high-dimensional similarity search with music information retrieval applications. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/3515

Chicago Manual of Style (16th Edition):

Faghfouri, Aidin. “Distributed high-dimensional similarity search with music information retrieval applications.” 2011. Masters Thesis, University of Victoria. Accessed April 26, 2019. http://hdl.handle.net/1828/3515.

MLA Handbook (7th Edition):

Faghfouri, Aidin. “Distributed high-dimensional similarity search with music information retrieval applications.” 2011. Web. 26 Apr 2019.

Vancouver:

Faghfouri A. Distributed high-dimensional similarity search with music information retrieval applications. [Internet] [Masters thesis]. University of Victoria; 2011. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1828/3515.

Council of Science Editors:

Faghfouri A. Distributed high-dimensional similarity search with music information retrieval applications. [Masters Thesis]. University of Victoria; 2011. Available from: http://hdl.handle.net/1828/3515


Queensland University of Technology

15. Chappell, Timothy A. Scalable document hashing and retrieval.

Degree: 2015, Queensland University of Technology

 This thesis studies document signatures, which are small representations of documents and other objects that can be stored compactly and compared for similarity. This research… (more)

Subjects/Keywords: Information retrieval; Document signatures; Signature files; Relevance feedback; Superimposed coding; Locality-sensitive hashing; Topological signatures; Dimensionality reduction; Nearest-neighbour; Hamming distance problem

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chappell, T. A. (2015). Scalable document hashing and retrieval. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/90044/

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):

Chappell, Timothy A. “Scalable document hashing and retrieval.” 2015. Thesis, Queensland University of Technology. Accessed April 26, 2019. https://eprints.qut.edu.au/90044/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chappell, Timothy A. “Scalable document hashing and retrieval.” 2015. Web. 26 Apr 2019.

Vancouver:

Chappell TA. Scalable document hashing and retrieval. [Internet] [Thesis]. Queensland University of Technology; 2015. [cited 2019 Apr 26]. Available from: https://eprints.qut.edu.au/90044/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chappell TA. Scalable document hashing and retrieval. [Thesis]. Queensland University of Technology; 2015. Available from: https://eprints.qut.edu.au/90044/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

16. Dong, Wei. High-Dimensional Similarity Search for Large Datasets .

Degree: PhD, 2011, Princeton University

 The volume of images and other non-text data is growing exponentially in today's digital universe. A popular way of extracting useful information from such data… (more)

Subjects/Keywords: k-nearest neighbor; locality-sensitive hashing; near-duplicate; sketch

…by recursively partitioning the dataset; 7 Locality-Sensitive Hashing involves… …Modeling and Improving Multi-Probe LSH Although Locality-Sensitive Hashing (LSH) has… …existing method. 2.1 Introduction Locality-Sensitive Hashing (LSH) [36, 31, 20… …well. 2.2 2.2.1 Background Locality-Sensitive Hashing LSH was introduced as a randomized… …tolerable. Most recent works fall within the following three categories. Locality Sensitive… 

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APA (6th Edition):

Dong, W. (2011). High-Dimensional Similarity Search for Large Datasets . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01pc289j087

Chicago Manual of Style (16th Edition):

Dong, Wei. “High-Dimensional Similarity Search for Large Datasets .” 2011. Doctoral Dissertation, Princeton University. Accessed April 26, 2019. http://arks.princeton.edu/ark:/88435/dsp01pc289j087.

MLA Handbook (7th Edition):

Dong, Wei. “High-Dimensional Similarity Search for Large Datasets .” 2011. Web. 26 Apr 2019.

Vancouver:

Dong W. High-Dimensional Similarity Search for Large Datasets . [Internet] [Doctoral dissertation]. Princeton University; 2011. [cited 2019 Apr 26]. Available from: http://arks.princeton.edu/ark:/88435/dsp01pc289j087.

Council of Science Editors:

Dong W. High-Dimensional Similarity Search for Large Datasets . [Doctoral Dissertation]. Princeton University; 2011. Available from: http://arks.princeton.edu/ark:/88435/dsp01pc289j087


University of Pretoria

17. Van Wyk, Frans-Pieter. Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments.

Degree: Electrical, Electronic and Computer Engineering, 2013, University of Pretoria

 Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into… (more)

Subjects/Keywords: Object recognition; Pose estimation; Real-time; Partial object matching; 3D features; Free form deformation; Data compression; Locality sensitive hashing; Structured light; Intelligent systems; UCTD

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APA (6th Edition):

Van Wyk, F. (2013). Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/33323

Chicago Manual of Style (16th Edition):

Van Wyk, Frans-Pieter. “Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments.” 2013. Masters Thesis, University of Pretoria. Accessed April 26, 2019. http://hdl.handle.net/2263/33323.

MLA Handbook (7th Edition):

Van Wyk, Frans-Pieter. “Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments.” 2013. Web. 26 Apr 2019.

Vancouver:

Van Wyk F. Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments. [Internet] [Masters thesis]. University of Pretoria; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2263/33323.

Council of Science Editors:

Van Wyk F. Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments. [Masters Thesis]. University of Pretoria; 2013. Available from: http://hdl.handle.net/2263/33323


University of Newcastle

18. Walker, Josiah. Improved similarity search for large data in machine learning and robotics.

Degree: PhD, 2017, University of Newcastle

Research Doctorate - Doctor of Philosophy (PhD)

This thesis presents techniques for accelerating similarity search methods on large datasets. Similarity search has applications in clustering,… (more)

Subjects/Keywords: nearest neighbour search; cover trees; approximate search; locality sensitive hashing; boosting; learning to search; similarity and distance learning; big data; large scale learning; reverse nearest neighbour search

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Walker, J. (2017). Improved similarity search for large data in machine learning and robotics. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1333823

Chicago Manual of Style (16th Edition):

Walker, Josiah. “Improved similarity search for large data in machine learning and robotics.” 2017. Doctoral Dissertation, University of Newcastle. Accessed April 26, 2019. http://hdl.handle.net/1959.13/1333823.

MLA Handbook (7th Edition):

Walker, Josiah. “Improved similarity search for large data in machine learning and robotics.” 2017. Web. 26 Apr 2019.

Vancouver:

Walker J. Improved similarity search for large data in machine learning and robotics. [Internet] [Doctoral dissertation]. University of Newcastle; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1959.13/1333823.

Council of Science Editors:

Walker J. Improved similarity search for large data in machine learning and robotics. [Doctoral Dissertation]. University of Newcastle; 2017. Available from: http://hdl.handle.net/1959.13/1333823


Pontifical Catholic University of Rio de Janeiro

19. DANIEL ALEJANDRO MESEJO-LEON. [en] APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE.

Degree: 2018, Pontifical Catholic University of Rio de Janeiro

[pt] Em uma série de aplicações, os pontos de dados podem ser representados como distribuições de probabilidade. Por exemplo, os documentos podem ser representados como… (more)

Subjects/Keywords: [pt] DIVERGENCIA KULLBACK-LEIBER; [en] KULLBACK-LEIBLER DIVERGENCE; [pt] BUSCA DE VIZINHOS MAIS PROXIMOS; [en] NEAREST NEIGHBOR SEARCH; [pt] INDICES INVERTIDOS; [en] INVERTED INDEX; [pt] HASH SENSIVEL A LOCALIDADE; [en] LOCALITY SENSITIVE HASHING; [pt] ARVORES DE BREGMAN; [en] BREGMAN BALL TREE

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APA (6th Edition):

MESEJO-LEON, D. A. (2018). [en] APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33305

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):

MESEJO-LEON, DANIEL ALEJANDRO. “[en] APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE.” 2018. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed April 26, 2019. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33305.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

MESEJO-LEON, DANIEL ALEJANDRO. “[en] APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE.” 2018. Web. 26 Apr 2019.

Vancouver:

MESEJO-LEON DA. [en] APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. [cited 2019 Apr 26]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33305.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

MESEJO-LEON DA. [en] APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33305

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

20. Wan, Shaohua. A scalable metric learning based voting method for expression recognition.

Degree: Electrical and Computer Engineering, 2013, University of Texas – Austin

 In this research work, we propose a facial expression classification method using metric learning-based k-nearest neighbor voting. To achieve accurate classification of a facial expression… (more)

Subjects/Keywords: Facial expression recognition; k Nearest Neighbor; Metric learning; Locality sensitive hashing

…set, Locality Sensitive Hashing (LSH) [64] is adopted to increase the… …locality sensitive hashing scheme is a distribution on a family hash functions operating on a… …database, a variant of the ML-based kNN voting method [24] based on Locality Sensitive… …adhere to the definition in [64]. Given a locality sensitive hash function family… …3 Hashing (LSH) [7] is used to speed up the kNN search process. This… 

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APA (6th Edition):

Wan, S. (2013). A scalable metric learning based voting method for expression recognition. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/21521

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):

Wan, Shaohua. “A scalable metric learning based voting method for expression recognition.” 2013. Thesis, University of Texas – Austin. Accessed April 26, 2019. http://hdl.handle.net/2152/21521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wan, Shaohua. “A scalable metric learning based voting method for expression recognition.” 2013. Web. 26 Apr 2019.

Vancouver:

Wan S. A scalable metric learning based voting method for expression recognition. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/2152/21521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wan S. A scalable metric learning based voting method for expression recognition. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/21521

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

21. Kletzander, Martin. Vyhledávání opakovaných záběrů .

Degree: 2010, Brno University of Technology

 Tato práce popisuje metody vyhledávání podle obsahu. Vybranou metodu dále aplikuje na vyhledávání podobných obrázků a snímků ve videu. Práce dále kvalifikuje metody používané na… (more)

Subjects/Keywords: Vyhledávání podle obsahu; LBP; Lokální binární vzory; HOG; Histogramy orientovaných gradientů; LSH; Hashování podle pozice; min-Hash; barevné histogramy; Content-based Search; LBP; Local Binary Patterns; HOG; Histograms of Oriented Gradients; LSH; Locality Sensitive Hashing; min-Hash; Color Histograms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kletzander, M. (2010). Vyhledávání opakovaných záběrů . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/56034

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):

Kletzander, Martin. “Vyhledávání opakovaných záběrů .” 2010. Thesis, Brno University of Technology. Accessed April 26, 2019. http://hdl.handle.net/11012/56034.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kletzander, Martin. “Vyhledávání opakovaných záběrů .” 2010. Web. 26 Apr 2019.

Vancouver:

Kletzander M. Vyhledávání opakovaných záběrů . [Internet] [Thesis]. Brno University of Technology; 2010. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/11012/56034.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kletzander M. Vyhledávání opakovaných záběrů . [Thesis]. Brno University of Technology; 2010. Available from: http://hdl.handle.net/11012/56034

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

22. Carraher, Lee A. A Parallel Algorithm for Query Adaptive, Locality Sensitive Hash Search.

Degree: MS, Engineering and Applied Science: Computer Science, 2012, University of Cincinnati

 Nearest neighbor search is a fundamental requirement of many machine learning algorithms and is essential to fuzzy information retrieval. The utility of efficient database search… (more)

Subjects/Keywords: Computer Science; Locality Sensitive Hashing; Approximate Nearest Neighbors; CUDA GPU; Image Search; Distance Adaptive LSH; Parallel Computing

…31 4.1.4 4.2 A Locality Sensitive Hash Function… …83 7.3.2 Where Locality Sensitive Hash Functions Really Shine… …focus of this thesis and it is a lattice based extension of locality sensitive hash based… …Andoni and Indyk locality sensitive hash based nearest neighbor search algorithm with a leech… …sensitive hashing methods as the δ−approximation is related to ρ for the probability of near and… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Sample image

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Carraher, L. A. (2012). A Parallel Algorithm for Query Adaptive, Locality Sensitive Hash Search. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337886738

Chicago Manual of Style (16th Edition):

Carraher, Lee A. “A Parallel Algorithm for Query Adaptive, Locality Sensitive Hash Search.” 2012. Masters Thesis, University of Cincinnati. Accessed April 26, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337886738.

MLA Handbook (7th Edition):

Carraher, Lee A. “A Parallel Algorithm for Query Adaptive, Locality Sensitive Hash Search.” 2012. Web. 26 Apr 2019.

Vancouver:

Carraher LA. A Parallel Algorithm for Query Adaptive, Locality Sensitive Hash Search. [Internet] [Masters thesis]. University of Cincinnati; 2012. [cited 2019 Apr 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337886738.

Council of Science Editors:

Carraher LA. A Parallel Algorithm for Query Adaptive, Locality Sensitive Hash Search. [Masters Thesis]. University of Cincinnati; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337886738

23. Yan, Lin. REGION-BASED GEOMETRIC ACTIVE CONTOUR FOR CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES.

Degree: PhD, Geodetic Science and Surveying, 2011, The Ohio State University

  The high spectral resolution of hyperspectral imaging (HSI) systems greatly enhances the capabilities of discrimination, identification and quantification of objects of different materials from… (more)

Subjects/Keywords: Remote Sensing; spectral-spatail classification; region-based active contour; hyperspectral; dimensionality reduction; locality-sensitive hashing; Laplacian eigenmaps

…algorithm of fast nearest neighbor search in high-dimensional spaces, locality-sensitive hashing… 

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APA (6th Edition):

Yan, L. (2011). REGION-BASED GEOMETRIC ACTIVE CONTOUR FOR CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1315344636

Chicago Manual of Style (16th Edition):

Yan, Lin. “REGION-BASED GEOMETRIC ACTIVE CONTOUR FOR CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES.” 2011. Doctoral Dissertation, The Ohio State University. Accessed April 26, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315344636.

MLA Handbook (7th Edition):

Yan, Lin. “REGION-BASED GEOMETRIC ACTIVE CONTOUR FOR CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES.” 2011. Web. 26 Apr 2019.

Vancouver:

Yan L. REGION-BASED GEOMETRIC ACTIVE CONTOUR FOR CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES. [Internet] [Doctoral dissertation]. The Ohio State University; 2011. [cited 2019 Apr 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1315344636.

Council of Science Editors:

Yan L. REGION-BASED GEOMETRIC ACTIVE CONTOUR FOR CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES. [Doctoral Dissertation]. The Ohio State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1315344636

24. 福田, 真啓. 大規模な音楽指紋データベースの高速検索におけるクエリの歪みへの頑健性向上に関する調査研究.

Degree: Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学

Supervisor: 井口寧

情報科学研究科

修士

Subjects/Keywords: FPGA; 音楽指紋; LSH; 千万曲分の乱数データベース; Music Fingerprint; Locality Sensitive Hashing; Database of Ten Million Musics

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APA (6th Edition):

福田, . (n.d.). 大規模な音楽指紋データベースの高速検索におけるクエリの歪みへの頑健性向上に関する調査研究. (Thesis). Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10119/12925

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

福田, 真啓. “大規模な音楽指紋データベースの高速検索におけるクエリの歪みへの頑健性向上に関する調査研究.” Thesis, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Accessed April 26, 2019. http://hdl.handle.net/10119/12925.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

福田, 真啓. “大規模な音楽指紋データベースの高速検索におけるクエリの歪みへの頑健性向上に関する調査研究.” Web. 26 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

福田 . 大規模な音楽指紋データベースの高速検索におけるクエリの歪みへの頑健性向上に関する調査研究. [Internet] [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; [cited 2019 Apr 26]. Available from: http://hdl.handle.net/10119/12925.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

福田 . 大規模な音楽指紋データベースの高速検索におけるクエリの歪みへの頑健性向上に関する調査研究. [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; Available from: http://hdl.handle.net/10119/12925

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

25. Chakrabarti, Aniket. Scaling Analytics via Approximate and Distributed Computing.

Degree: PhD, Computer Science and Engineering, 2017, The Ohio State University

 The amount data generated from different sources everyday is increasing exponentially and for businesses to generate actionable insights, it is paramount that the analyses complete… (more)

Subjects/Keywords: Computer Science; Approximate Computing; Distributed Computing; Locality Sensitive Hashing; Kernel Learning; Markov Random Field; Pareto Frontier; Analytics Frameworks

…Parthasarathy. Improving Locality Sensitive Hashing Based Similarity Search and Estimation for Kernels… …Srinivasan Parthasarathy. A bayesian perspective on locality sensitive hashing with extensions for… …Sequential hypothesis tests for adaptive locality sensitive hashing. In Proceedings of the 24th… …Computing through Locality Sensitive Hashing 1.4.2 Distributed Computing for Scaling Analytics… …Background . . . . . . . . . . . . . . . . . . . . 2.2.1 Locality Sensitive Hashing… 

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APA (6th Edition):

Chakrabarti, A. (2017). Scaling Analytics via Approximate and Distributed Computing. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1500473400586782

Chicago Manual of Style (16th Edition):

Chakrabarti, Aniket. “Scaling Analytics via Approximate and Distributed Computing.” 2017. Doctoral Dissertation, The Ohio State University. Accessed April 26, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500473400586782.

MLA Handbook (7th Edition):

Chakrabarti, Aniket. “Scaling Analytics via Approximate and Distributed Computing.” 2017. Web. 26 Apr 2019.

Vancouver:

Chakrabarti A. Scaling Analytics via Approximate and Distributed Computing. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2019 Apr 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1500473400586782.

Council of Science Editors:

Chakrabarti A. Scaling Analytics via Approximate and Distributed Computing. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1500473400586782

26. Visser, M. Feature Fusion for Efficient Content-Based Video Retrieval:.

Degree: 2013, Delft University of Technology

 Abstract—Content-based video retrieval is a complex task because of the large amount of information in single items and because databases of videos can be very… (more)

Subjects/Keywords: content-based video retrieval; feature fusion; metric learning; efficient retrieval; nearest neighbor search; locality sensitive hashing; vantage point trees

…algorithm, Vantage Point trees and Locality Sensitive Hashing. Both reduce the number of exact… …hashing algorithms for approximate nearest neighbor in high dimensions. In Foundations of… 

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APA (6th Edition):

Visser, M. (2013). Feature Fusion for Efficient Content-Based Video Retrieval:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:de8abdb6-2038-4fba-90fd-13667abdd930

Chicago Manual of Style (16th Edition):

Visser, M. “Feature Fusion for Efficient Content-Based Video Retrieval:.” 2013. Masters Thesis, Delft University of Technology. Accessed April 26, 2019. http://resolver.tudelft.nl/uuid:de8abdb6-2038-4fba-90fd-13667abdd930.

MLA Handbook (7th Edition):

Visser, M. “Feature Fusion for Efficient Content-Based Video Retrieval:.” 2013. Web. 26 Apr 2019.

Vancouver:

Visser M. Feature Fusion for Efficient Content-Based Video Retrieval:. [Internet] [Masters thesis]. Delft University of Technology; 2013. [cited 2019 Apr 26]. Available from: http://resolver.tudelft.nl/uuid:de8abdb6-2038-4fba-90fd-13667abdd930.

Council of Science Editors:

Visser M. Feature Fusion for Efficient Content-Based Video Retrieval:. [Masters Thesis]. Delft University of Technology; 2013. Available from: http://resolver.tudelft.nl/uuid:de8abdb6-2038-4fba-90fd-13667abdd930

27. Pham, The Anh. Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space.

Degree: Docteur es, Informatique, 2013, Université François-Rabelais de Tours

Les caractéristiques locales sont essentielles dans de nombreux domaines de l’analyse d’images comme la détection et la reconnaissance d’objets, la recherche d’images, etc. Ces dernières… (more)

Subjects/Keywords: Détection de jonctions; Caractérisation de jonctions; Détection de points d’intérêt; Documents graphiques; Images de trait; Recherche approximative de plus proches voisins; Indexation de caractéristiques; Arbres de clustering; Junction detection; Junction characterization; Junction distortion; Topology correction; Edge grouping; Dominant point detection; Graphical documents; Line- drawings; Approximate nearest neighbor search; Feature indexing; Locality-sensitive hashing; Clustering trees

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pham, T. A. (2013). Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space. (Doctoral Dissertation). Université François-Rabelais de Tours. Retrieved from http://www.theses.fr/2013TOUR4023

Chicago Manual of Style (16th Edition):

Pham, The Anh. “Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space.” 2013. Doctoral Dissertation, Université François-Rabelais de Tours. Accessed April 26, 2019. http://www.theses.fr/2013TOUR4023.

MLA Handbook (7th Edition):

Pham, The Anh. “Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space.” 2013. Web. 26 Apr 2019.

Vancouver:

Pham TA. Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space. [Internet] [Doctoral dissertation]. Université François-Rabelais de Tours; 2013. [cited 2019 Apr 26]. Available from: http://www.theses.fr/2013TOUR4023.

Council of Science Editors:

Pham TA. Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space. [Doctoral Dissertation]. Université François-Rabelais de Tours; 2013. Available from: http://www.theses.fr/2013TOUR4023

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