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Asymmetric Learning and Dissimilarity Spaces for Content-based Retrieval

Presented at Tempe (AZ), July 13-15
  • Lecture Notes in Computer Science; 4071
Publication date2006

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.

  • Computer Graphics
  • Information Storage and Retrieval
  • Database Management
  • Information Systems Applications (incl. Internet)
  • Multimedia Information Systems
  • Image Processing and Computer Vision
Citation (ISO format)
BRUNO, Eric, MOENNE-LOCCOZ, Nicolas, MARCHAND-MAILLET, Stéphane. Asymmetric Learning and Dissimilarity Spaces for Content-based Retrieval. In: Image and Video Retrieval : 5th International Conference, CIVR 2006. Proceedings. Tempe (AZ). [s.l.] : Springer, 2006. p. 330–339. (Lecture Notes in Computer Science) doi: 10.1007/11788034_34
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