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Unsupervised Modeling and Recognition of Object Categories with Combination of Visual Contents and Geometric Similarity Links

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posted on 01.01.2008, 00:00 by Gunhee Kim, Christos Faloutsos, Martial Hebert
This paper proposes a probabilistic approach for unsupervised modeling and recognition of object categories which combines two types of complementary visual evidence, visual contents and inter-connected links between the images. By doing so, our approach not only increases modeling and recognition performance but also provides possible solutions to several problems including modeling of geometric information, computational complexity, and the inherent ambiguity of visual words. Our approach can be incorporated in any generative models, but here we consider two popular models, pLSA and LDA. Experimental results show that the topic models updated by adding link analysis terms significantly improve the standard pLSA and LDA models. Furthermore, we presented competitive performances on unsupervised modeling, ranking of training images, classification of unseen images, and localization tasks with MSRC and PASCAL2005 datasets.


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Copyright © 2008 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or © ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in teh Proceeding of the 1st ACM international conference on Multimedia information retrieval {Proceeding of the 1st ACM international conference on Multimedia information retrieval (2008)}



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