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Score Distribution Approach to Automatic Kernel Selection for Image Retrieval Systems

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Foundations of Intelligent Systems (ISMIS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4203))

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Abstract

This paper introduces a kernel selection method to automatically choose the best kernel type for a query by using the score distributions of the relevant and non-relevant images given by user as feedback. When applied to our data, the method selects the same best kernel (out of the 12 tried kernels) for a particular query as the kernel obtained from our extensive experimental results.

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Author information

Authors and Affiliations

  1. University of Louisiana at Lafayette, Lafayette, LA, 70504, USA

    Anca Doloc-Mihu & Vijay V. Raghavan

Authors
  1. Anca Doloc-Mihu
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  2. Vijay V. Raghavan
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari,  

    Floriana Esposito

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Dipartimento di Informatica, Università degli Studi di Bari, via Orabona, 4, 70126, Bari, Italy

    Donato Malerba

  4. Dipartimento di Informatica, Università di Bari, Via E. Orabona, 4, 70125, Bari, Italia

    Giovanni Semeraro

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© 2006 Springer-Verlag Berlin Heidelberg

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Doloc-Mihu, A., Raghavan, V.V. (2006). Score Distribution Approach to Automatic Kernel Selection for Image Retrieval Systems. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_27

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  • DOI: https://doi.org/10.1007/11875604_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45764-0

  • Online ISBN: 978-3-540-45766-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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