Using Score Distribution Models to Select the Kernel Type for a Web-Based Adaptive Image Retrieval System (AIRS)

  • Anca Doloc-Mihu
  • Vijay V. Raghavan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


The goal of this paper is to investigate the selection of the kernel for a Web-based AIRS. Using the Kernel Rocchio learning method, several kernels having polynomial and Gaussian forms are applied to general images represented by color histograms in RGB and HSV color spaces. Experimental results on these collections show that performance varies significantly between different kernel types and that choosing an appropriate kernel is important. Then, based on these results, we propose a method for selecting the kernel type that uses the score distribution models. Experimental results on our data show that the proposed method is effective for our system.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anca Doloc-Mihu
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.University of Louisiana at LafayetteLafayetteUSA

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