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Unity in diversity: Discovering topics from words: Information theoretic co-clustering for visual categorization

Gupta, A and Bowden, R (2012) Unity in diversity: Discovering topics from words: Information theoretic co-clustering for visual categorization In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2012-02-24 - 2012-02-26, Rome. (Unpublished)

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Abstract

This paper presents a novel approach to learning a codebook for visual categorization, that resolves the key issue of intra-category appearance variation found in complex real world datasets. The codebook of visual-topics (semantically equivalent descriptors) is made by grouping visual-words (syntactically equivalent descriptors) that are scattered in feature space. We analyze the joint distribution of images and visual-words using information theoretic co-clustering to discover visual-topics. Our approach is compared with the standard 'Bagof- Words' approach. The statistically significant performance improvement in all the datasets utilized (Pascal VOC 2006; VOC 2007; VOC 2010; Scene-15) establishes the efficacy of our approach.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Gupta, AUNSPECIFIEDUNSPECIFIED
Bowden, RUNSPECIFIEDUNSPECIFIED
Date : 2012
Additional Information : This is the author's version of the paper.
Depositing User : Symplectic Elements
Date Deposited : 17 Nov 2015 18:49
Last Modified : 17 Nov 2015 18:49
URI: http://epubs.surrey.ac.uk/id/eprint/808981

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