Bart, Evgeniy and Porteous, Ian and Perona, Pietro and Welling , Max (2008) Unsupervised learning of visual taxonomies. In: Computer Vision and Pattern Recognition, 2008, CVPR 2008, IEEE Conference on : 23-28 June 2008. Proceedings – IEEE Computer Society Conference on Computer Vision and Pattern Recognition . , pp. 2166-2173. ISBN 978-1-4244-2242-5 http://resolver.caltech.edu/CaltechAUTHORS:20100715-133158050
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As more images and categories become available, organizing them becomes crucial. We present a novel statistical method for organizing a collection of images into a treeshaped hierarchy. The method employs a non-parametric Bayesian model and is completely unsupervised. Each image is associated with a path through a tree. Similar images share initial segments of their paths and therefore have a smaller distance from each other. Each internal node in the hierarchy represents information that is common to images whose paths pass through that node, thus providing a compact image representation. Our experiments show that a disorganized collection of images will be organized into an intuitive taxonomy. Furthermore, we find that the taxonomy allows good image categorization and, in this respect, is superior to the popular LDA model.
|Item Type:||Book Section|
|Additional Information:||© 2008 IEEE. This material is based upon work supported by the National Science Foundation under Grants No. 0447903, No. 0535278 and IIS-0535292, and by ONR MURI grant 00014-06-1-0734.|
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|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||04 Aug 2010 18:19|
|Last Modified:||26 Dec 2012 12:14|
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