Asymmetric Learning and Dissimilarity Spaces for Content-Based Retrieval

  • Eric Bruno
  • Nicolas Moenne-Loccoz
  • Stéphane Marchand-Maillet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eric Bruno
    • 1
  • Nicolas Moenne-Loccoz
    • 1
  • Stéphane Marchand-Maillet
    • 1
  1. 1.Viper group, Computer Vision and Multimedia LaboratoryUniversity of Geneva 

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