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CIVR
2006
Springer

Asymmetric Learning and Dissimilarity Spaces for Content-Based Retrieval

10 years 3 months ago
Asymmetric Learning and Dissimilarity Spaces for Content-Based Retrieval
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 stateof-the-art algorithms.
Eric Bruno, Nicolas Moënne-Loccoz, Sté
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CIVR
Authors Eric Bruno, Nicolas Moënne-Loccoz, Stéphane Marchand-Maillet
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