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Title: Semantic segmentation and image search
Author: Johnson, M. A.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2008
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Understanding the meaning behind visual data is increasingly important as the quantity of digital images in circulation explodes, and as computing in general and the Internet in specific shifts quickly towards an increasingly visual presentation of data. However, the remarkable amount of variance inside categories (e.g. different kinds of chairs) combined with the occurrence of similarity between categories (e.g. similar breeds of cats and dogs) makes this problem incredibly difficult to solve. In particular, the semantic segmentation of images into contiguous regions of similar interpretation combines the difficulties of object recognition and image segmentation to result in a problem of great complexity, yet great reward. This thesis proposes a novel solution to the problem of semantic segmentation, and explores its application to image search and retrieval. Our primary contribution is a new image information processing tool: the semantic texton forest. We use semantic texton forests to perform (i) semantic segmentation of images and (ii) image categorization, achieving state-of-the-art results for both on two challenging datasets. We then apply this to the problem of image search and retrieval, resulting in the Palette Search System. With Palette Search, the user is able to search for the first time using Query by Semantic Composition, in which he communicates both what he wants in the result image and where he wants it.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available