Asymmetric learning and dissimilarity spaces for content-based retrieval

Authors:
Eric Bruno;Nicolas Moenne-Loccoz;Stéphane Marchand-Maillet
Affiliations:
Viper group, Computer Vision and Multimedia Laboratory, University of Geneva;Viper group, Computer Vision and Multimedia Laboratory, University of Geneva;Viper group, Computer Vision and Multimedia Laboratory, University of Geneva
Venue:
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Year:
2006

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Abstract

This paper presents novel dissimilarity space specially designed for interactive multimedia retrieval. By providing queries made of positive and negative examples, the goal consists in learning the positive class distribution. This classification problem is known to be asymmetric, i.e. the negative class does not cluster in the original feature spaces. We introduce here the idea of Query-based Dissimilarity Space (QDS) which enables to cope with the asymmetrical setup by converting it in a more classical 2-class problem. The proposed approach is evaluated on both artificial data and real image database, and compared with state-of-the-art algorithms.