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Image Kernels

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Pattern Recognition with Support Vector Machines (SVM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2388))

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Abstract

In this paper we discuss the mathematical properties of a few kernels specifically constructed for dealing with image data in binary classification and novelty detection problems. First, we show that histogram intersection is a Mercer’s kernel. Then, we show that a similarity measure based on the notion of Hausdorff distance and directly applicable to raw images, though not a Mercer’s kernel, is a kernel for novelty detection. Both kernels appear to be well suited for building effective vision-based learning systems.

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Author information

Authors and Affiliations

  1. INFM - DISI, Università di Genova, Genova, Italy

    Annalisa Barla, Emanuele Franceschi, Francesca Odone & Alessandro Verri

Authors
  1. Annalisa Barla
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  2. Emanuele Franceschi
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  3. Francesca Odone
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  4. Alessandro Verri
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Alessandro Verri

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© 2002 Springer-Verlag Berlin Heidelberg

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Barla, A., Franceschi, E., Odone, F., Verri, A. (2002). Image Kernels. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_7

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  • DOI: https://doi.org/10.1007/3-540-45665-1_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44016-1

  • Online ISBN: 978-3-540-45665-0

  • eBook Packages: Springer Book Archive

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