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University of North Carolina

1. Ordonez Roman, Vicente. Language and Perceptual Categorization in Computational Visual Recognition.

Degree: Computer Science, 2015, University of North Carolina

Computational visual recognition or giving computers the ability to understand images as well as humans do is a core problem in Computer Vision. Traditional recognition systems often describe visual content by producing a set of isolated labels, object locations, or by even trying to annotate every pixel in an image with a category. People instead describe the visual world using language. The rich visually descriptive language produced by people incorporates information from human intuition, world knowledge, visual saliency, and common sense that go beyond detecting individual visual concepts like objects, attributes, or scenes. Moreover, due to the rising popularity of social media, there exist billions of images with associated text on the web, yet systems that can leverage this type of annotations or try to connect language and vision are scarce. In this dissertation, we propose new approaches that explore the connections between language and vision at several levels of detail by combining techniques from Computer Vision and Natural Language Understanding. We first present a data-driven technique for understanding and generating image descriptions using natural language, including automatically collecting a big-scale dataset of images with visually descriptive captions. Then we introduce a system for retrieving short visually descriptive phrases for describing some part or aspect of an image, and a simple technique to generate full image descriptions by stitching short phrases. Next we introduce an approach for collecting and generating referring expressions for objects in natural scenes at a much larger scale than previous studies. Finally, we describe methods for learning how to name objects by using intuitions from perceptual categorization related to basic-level and entry-level categories. The main contribution of this thesis is in advancing our knowledge on how to leverage language and intuitions from human perception to create visual recognition systems that can better learn from and communicate with people. Advisors/Committee Members: Ordonez Roman, Vicente, Berg, Tamara, Berg, Alexander, Efros, Alexei, Choi, Yejin, Frahm, Jan-Michael.

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

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APA (6th Edition):

Ordonez Roman, V. (2015). Language and Perceptual Categorization in Computational Visual Recognition. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6

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):

Ordonez Roman, Vicente. “Language and Perceptual Categorization in Computational Visual Recognition.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6.

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

MLA Handbook (7th Edition):

Ordonez Roman, Vicente. “Language and Perceptual Categorization in Computational Visual Recognition.” 2015. Web. 18 Jan 2021.

Vancouver:

Ordonez Roman V. Language and Perceptual Categorization in Computational Visual Recognition. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6.

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

Council of Science Editors:

Ordonez Roman V. Language and Perceptual Categorization in Computational Visual Recognition. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6

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

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