Skip to main content
Springer Nature Link
Log in

The GCS Kernel for SVM-Based Image Recognition

  • Conference paper
Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

Included in the following conference series:

  • 2907 Accesses

Abstract

In this paper, we present a new compactly supported kernel for SVM based image recognition. This kernel which we called Geometric Compactly Supported (GCS) can be viewed as a generalization of spherical kernels to higher dimensions. The construction of the GCS kernel is based on a geometric approach using the intersection volume of two n-dimensional balls. The compactness property of the GCS kernel leads to a sparse Gram matrix which enhances computation efficiency by using sparse linear algebra algorithms. Comparisons of the GCS kernel performance, for image recognition task, with other known kernels prove the interest of this new kernel.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Gentile, C.: A new approximate maximal margin classification algorithm. Journal of Machine Learning Research 2, 213–242 (2001)

    Article  MathSciNet  Google Scholar 

  2. Genton, M.: Classes of kernels for machine learning: a statistics perspective. Journal of Machine Learning Research 2, 299–312 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  3. Cressie, N.: Statistics for Spatial Data. Wiley, New York (1993)

    Google Scholar 

  4. Hamers, B., Suykens, J.A.K., De Moor, B.: Compactly supported rbf kernels for sparsifying the gram matrix in ls-svm regression models. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 720–726. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Gilbert, J., Moler, C., Schreiber, R.: Sparse matrices in matlab: design and implementation. SIAM Journal on Matrix Analysis 13(1), 333–356 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  6. Chapelle, O., Haffner, P., Vapnik, V.: Svms for histogram-based image classification. IEEE Transactions on Neural Networks 9 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IMEDIA Group, INRIA Rocquencourt, 78153, Le Chesnay, France

    Sabri Boughorbel & Nozha Boujemaa

  2. DESE, LCPC, 58 Bd Lefebvre, 75015, Paris, France

    Jean-Philippe Tarel

  3. CVLAB, EPFL, 1015, Lausanne, Switzerland

    François Fleuret

Authors
  1. Sabri Boughorbel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean-Philippe Tarel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. François Fleuret
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nozha Boujemaa
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Boughorbel, S., Tarel, JP., Fleuret, F., Boujemaa, N. (2005). The GCS Kernel for SVM-Based Image Recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_94

Download citation

  • DOI: https://doi.org/10.1007/11550907_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation