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You searched for +publisher:"University of North Carolina" +contributor:("Piramuthu, Robinson"). One record found.

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University of North Carolina

1. 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

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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

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