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Enhancing Visual Concept Detection by a Novel Matrix Modular Scheme on SVM

  • Conference paper
Evaluating Systems for Multilingual and Multimodal Information Access (CLEF 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5706))

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Abstract

A novel Matrix Modular Support Vector Machine(MMSVM) classifier is proposed to partition a visual concept problem into many easier two-class problems.This MMSVM shows significant detection improvements on the ImageClef2008 VCDT task, with a relative reduction of 15% of the classification error, compared with usual SVMs.

Work supported by French National Agency of Research ANR-06-MDCA-002, and Research Fund for the Doctoral Program of Higher Education of China 200803591024.

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Author information

Authors and Affiliations

  1. Systems and Information Sciences Lab. (LSIS), UMR CNRS &, Univ. Sud Toulon-Var, France

    Zhong-Qiu Zhao & Hervé Glotin

  2. Computer and Information School, Hefei Univ. of Technology, China

    Zhong-Qiu Zhao

Authors
  1. Zhong-Qiu Zhao
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  2. Hervé Glotin
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Editor information

Editors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione, CNR, Pisa, Italy

    Carol Peters

  2. RWTH Aachen University, Aachen, Germany

    Thomas Deselaers

  3. University of Padua, Padua, Italy

    Nicola Ferro

  4. LSI-UNED, Madrid, Spain

    Julio Gonzalo  & Anselmo Peñas  & 

  5. Dublin City University, Dublin 9, Ireland

    Gareth J. F. Jones

  6. Helsinki University of Technology, Espoo, Finland

    Mikko Kurimo

  7. University of Hildesheim, Hildesheim, Germany

    Thomas Mandl

  8. Humboldt University Berlin, Germany

    Vivien Petras

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© 2009 Springer-Verlag Berlin Heidelberg

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Zhao, ZQ., Glotin, H. (2009). Enhancing Visual Concept Detection by a Novel Matrix Modular Scheme on SVM. In: Peters, C., et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_80

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  • DOI: https://doi.org/10.1007/978-3-642-04447-2_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04446-5

  • Online ISBN: 978-3-642-04447-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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