Towards Automatic Detection of CBIRs Configuration

  • Christian Vilsmaier
  • Rolf Karp
  • Mario Döller
  • Harald Kosch
  • Lionel Brunie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7131)


Many Content Based Image Retrieval systems (CBIRs) have been invented in the last decade. The general mechanism of the search process is very similar for each of these CBIRs, and the calculation of rankings is determined by the comparison of features (low-, mid-, high-level). Nevertheless, all things being equal, the respective realization leads to different results. Knowledge about the internal configuration (used features, weights and metrics) of these systems would be beneficial in many usage scenarios (e.g., by using a query image content sensitive query forwarding strategy or improved result ranking strategies in meta search engines). In this context, the paper presents an approach that supports an automatic detection of the configuration of CBIR systems. We demonstrate that the problem can be partly traced back to an optimization problem and tested several optimization algorithms. The approach has been evaluated based on the ImageCLEF test set and shows good results.


CBIRs configuration Image Database Low-level Feature detection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Vilsmaier
    • 2
  • Rolf Karp
    • 1
  • Mario Döller
    • 1
  • Harald Kosch
    • 1
  • Lionel Brunie
    • 2
  1. 1.Distributed and Multimedia Information SytemsUniversity of PassauPassauGermany
  2. 2.INSA de Lyon, LIRISVilleurbanne CedexFrance

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