Chapter

Image and Video Retrieval

Volume 4071 of the series Lecture Notes in Computer Science pp 330-339

Asymmetric Learning and Dissimilarity Spaces for Content-Based Retrieval

  • Eric BrunoAffiliated withViper group, Computer Vision and Multimedia Laboratory, University of Geneva
  • , Nicolas Moenne-LoccozAffiliated withViper group, Computer Vision and Multimedia Laboratory, University of Geneva
  • , Stéphane Marchand-MailletAffiliated withViper group, Computer Vision and Multimedia Laboratory, University of Geneva

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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.