Online Image Retrieval System Using Long Term Relevance Feedback

  • Lutz Goldmann
  • Lars Thiele
  • Thomas Sikora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


This paper describes an original system for content based image retrieval. It is based on MPEG-7 descriptors and a novel approach for long term relevance feedback using a Bayesian classifier. Each image is represented by a special model that is adapted over multiple feedback rounds and even multiple sessions or users. The experiments show its outstanding performance in comparison to often used short term relevance feedback and the recently proposed FIRE system.


Feature Vector Image Retrieval Relevance Feedback Incremental Learning Content Base Image Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lutz Goldmann
    • 1
  • Lars Thiele
    • 1
  • Thomas Sikora
    • 1
  1. 1.Communication Systems GroupTechnical University of BerlinBerlinGermany

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