Epitome and its applications

Chu, Xinqi

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https://hdl.handle.net/2142/42279 Copy

Description

Title

Epitome and its applications

Author(s)

Chu, Xinqi

Issue Date

2013-02-03T19:30:08Z

Director of Research (if dissertation) or Advisor (if thesis)

Huang, Thomas S.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• probabilistic graphical models
• image synthesis
• recognition
• colorization

Abstract

Due to the lack of explicit spatial consideration, the existing epitome model may fail for image recognition and target detection, which directly motivates us to propose the so-called spatialized epitome in this thesis. Extended from the original simple graphical model of epitome, the spatialized epitome provides a general framework to integrate both appearance and spatial arrangement of patches in the image to achieve a more precise likelihood representation for image(s) and eliminate ambiguities in image reconstruction and recognition. From the extended graphical model of epitome, a new EM learning procedure is derived under the framework of variational approximation. The learning procedure can generate an optimized summary of the image appearance based on patches and automatically cluster the spatial distribution of the similar patches. From the spatialized epitome, we present a principled (parameter-free) way of inferring the probability of a new input image under the learned model and thereby enabling image recognition and target detection. We show how the incorporation of spatial information enhances the epitome’s ability for discrimination on several tough vision tasks, e.g., misalignment/cross-pose face recognition, and vehicle detection with a few training samples. We also apply this model to image colorization which not only increases the visual appeal of grayscale images, but also enriches the information contained in scientific images that lack color information. Most existing methods of colorization require laborious user interaction for scribbles or image segmentation. To eliminate the need for human labor, we develop an automatic image colorization method using epitome. Built upon a generative graphical model, epitome is a condensed image appearance and shape model which also proves to be an effective summary of color information for the colorization task. We train the epitome from the reference images and perform inference in the epitome to colorize grayscale images, rendering better colorization results than previous methods.

Graduation Semester

2012-12

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http://hdl.handle.net/2142/42279

Copyright and License Information

Copyright 2012 Xinqi Chu

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