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You searched for +publisher:"University of North Carolina" +contributor:("Lazebnik, Svetlana"). Showing records 1 – 3 of 3 total matches.

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University of North Carolina

1. Tighe, Joseph Patrick. Towards open-universe image parsing with broad coverage.

Degree: Computer Science, 2013, University of North Carolina

One of the main goals of computer vision is to develop algorithms that allow the computer to interpret an image not as a pattern of colors but as the semantic relationships that make up a real world three-dimensional scene. In this dissertation, I present a system for image parsing, or labeling the regions of an image with their semantic categories, as a means of scene understanding. Most existing image parsing systems use a fixed set of a few hundred hand-labeled images as examples from which they learn how to label image regions, but our world cannot be adequately described with only a few hundred images. A new breed of open universe datasets have recently started to emerge. These datasets not only have more images but are constantly expanding, with new images and labels assigned by users on the web. Here I present a system that is able to both learn from these larger datasets of labeled images and scale as the dataset expands, thus greatly broadening the number of class labels that can correctly be identified in an image. Throughout this work I employ a retrieval-based methodology: I first retrieve images similar to the query and then match image regions from this set of retrieved images. My system can assign to each image region multiple forms of meaning: for example, it can simultaneously label the wing of a crow as an animal, crow, wing, and feather. I also broaden the label coverage by using both region and detector based similarity measures to effectively match a broad range to label types. This work shows the power of retrieval-based systems and the importance of having a diverse set of image cues and interpretations. Advisors/Committee Members: Tighe, Joseph Patrick, Lazebnik, Svetlana.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Tighe, J. P. (2013). Towards open-universe image parsing with broad coverage. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:315a07bd-7741-4893-b95d-a31fcc7dee71

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):

Tighe, Joseph Patrick. “Towards open-universe image parsing with broad coverage.” 2013. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:315a07bd-7741-4893-b95d-a31fcc7dee71.

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

MLA Handbook (7th Edition):

Tighe, Joseph Patrick. “Towards open-universe image parsing with broad coverage.” 2013. Web. 18 Jan 2021.

Vancouver:

Tighe JP. Towards open-universe image parsing with broad coverage. [Internet] [Thesis]. University of North Carolina; 2013. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:315a07bd-7741-4893-b95d-a31fcc7dee71.

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

Council of Science Editors:

Tighe JP. Towards open-universe image parsing with broad coverage. [Thesis]. University of North Carolina; 2013. Available from: https://cdr.lib.unc.edu/record/uuid:315a07bd-7741-4893-b95d-a31fcc7dee71

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


University of North Carolina

2. Gong, Yunchao. Large-scale image retrieval using similarity preserving binary codes.

Degree: Computer Science, 2014, University of North Carolina

Image retrieval is a fundamental problem in computer vision, and has many applications. When the dataset size gets very large, retrieving images in Internet image collections becomes very challenging. The challenges come from storage, computation speed, and similarity representation. My thesis addresses learning compact similarity preserving binary codes, which represent each image by a short binary string, for fast retrieval in large image databases. I will first present an approach called Iterative Quantization to convert high-dimensional vectors to compact binary codes, which works by learning a rotation to minimize the quantization error of mapping data to the vertices of a binary Hamming cube. This approach achieves state-of-the-art accuracy for preserving neighbors in the original feature space, as well as state-of-the-art semantic precision. Second, I will extend this approach to two different scenarios in large-scale recognition and retrieval problems. The first extension is aimed at high-dimensional histogram data, such as bag-of-words features or text documents. Such vectors are typically sparse and nonnegative. I develop an algorithm that explores the special structure of such data by mapping feature vectors to binary vertices in the positive orthant, which gives improved performance. The second extension is for Fisher Vectors, which are dense descriptors having tens of thousands to millions of dimensions. I develop a novel method for converting such descriptors to compact similarity-preserving binary codes that exploits their natural matrix structure to reduce their dimensionality using compact bilinear projections instead of a single large projection matrix. This method achieves retrieval and classification accuracy comparable to that of the original descriptors and to the state-of-the-art Product Quantization approach while having orders of magnitude faster code generation time and smaller memory footprint. Finally, I present two applications of using Internet images and tags/labels to learn binary codes with label supervision, and show improved retrieval accuracy on several large Internet image datasets. First, I will present an application that performs cross-modal retrieval in the Hamming space. Then I will present an application on using supervised binary classeme representations for large-scale image retrieval. Advisors/Committee Members: Gong, Yunchao, Lazebnik, Svetlana, University of North Carolina at Chapel Hill.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Gong, Y. (2014). Large-scale image retrieval using similarity preserving binary codes. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:639dfed8-5877-4121-bdb2-b83b3dbd7489

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):

Gong, Yunchao. “Large-scale image retrieval using similarity preserving binary codes.” 2014. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:639dfed8-5877-4121-bdb2-b83b3dbd7489.

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

MLA Handbook (7th Edition):

Gong, Yunchao. “Large-scale image retrieval using similarity preserving binary codes.” 2014. Web. 18 Jan 2021.

Vancouver:

Gong Y. Large-scale image retrieval using similarity preserving binary codes. [Internet] [Thesis]. University of North Carolina; 2014. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:639dfed8-5877-4121-bdb2-b83b3dbd7489.

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

Council of Science Editors:

Gong Y. Large-scale image retrieval using similarity preserving binary codes. [Thesis]. University of North Carolina; 2014. Available from: https://cdr.lib.unc.edu/record/uuid:639dfed8-5877-4121-bdb2-b83b3dbd7489

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


University of North Carolina

3. Kiapour, Mohammadhadi. LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES.

Degree: Computer Science, 2015, University of North Carolina

Clothing recognition is a societally and commercially important yet extremely challenging problem due to large variations in clothing appearance, layering, style, body shape and pose. In this dissertation, we propose new computational vision approaches that learn to represent and recognize clothing items in images. First, we present an effective method for parsing clothing in fashion photographs, where we label the regions of an image with their clothing categories. We then extend our approach to tackle the clothing parsing problem using a data-driven methodology: for a query image, we find similar styles from a large database of tagged fashion images and use these examples to recognize clothing items in the query. Along with our novel large fashion dataset, we also present intriguing initial results on using clothing estimates to improve human pose identification. Second, we examine questions related to fashion styles and identifying the clothing elements associated with each style. We first design an online competitive style rating game called Hipster Wars to crowd source reliable human judgments of clothing styles. We use this game to collect a new dataset of clothing outfits with associated style ratings for different clothing styles. Next, we build visual style descriptors and train models that are able to classify clothing styles and identify the clothing elements are most discriminative in every style. Finally, we define a new task, Exact Street to Shop, where our goal is to match a real-world example of a garment item to the same exact garment in an online shop. This is an extremely challenging task due to visual differences between street photos that are taken of people wearing clothing in everyday uncontrolled settings, and online shop photos, which are captured by professionals in highly controlled settings. We introduce a novel large dataset for this application, collected from the web, and present a deep learning based similarity network that can compare clothing items across visual domains. Advisors/Committee Members: Kiapour, Mohammadhadi, Berg, Tamara, Berg, Tamara, Berg, Alexander, Lazebnik, Svetlana, Frahm, Jan-Michael, Piramuthu, Robinson.

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kiapour, M. (2015). LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775

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):

Kiapour, Mohammadhadi. “LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775.

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

MLA Handbook (7th Edition):

Kiapour, Mohammadhadi. “LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES.” 2015. Web. 18 Jan 2021.

Vancouver:

Kiapour M. LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775.

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

Council of Science Editors:

Kiapour M. LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775

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

.