Asymmetric Learning and Dissimilarity Spaces for Content-Based Retrieval | Zendy

Éric Bruno | Zendy; Nicolas Moënne-Loccoz | Zendy; Stéphane MarchandMaillet | Zendy

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Asymmetric Learning and Dissimilarity Spaces for Content-Based Retrieval

Author(s) -

Éric Bruno,

Nicolas Moënne-Loccoz,

Stéphane MarchandMaillet

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-36018-2

DOI - 10.1007/11788034_34

Subject(s) - computer science , class (philosophy) , feature vector , space (punctuation) , feature (linguistics) , image retrieval , content (measure theory) , artificial intelligence , content based image retrieval , cluster (spacecraft) , information retrieval , theoretical computer science , image (mathematics) , mathematics , philosophy , linguistics , operating system , mathematical analysis , programming language

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

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