Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion

  • Vadim Mottl
  • Alexander Tatarchuk
  • Valentina Sulimova
  • Olga Krasotkina
  • Oleg Seredin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4472)

Abstract

The problem of multi-modal pattern recognition is considered under the assumption that the kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output scales of single sensor is employed as an appropriate joint scale corresponding to the idea of combining modalities, actually, at the sensor level. From this point of view, the known kernel fusion techniques, including Relevance and Support Kernel Machines, offer a toolkit of combining pattern recognition modalities. We propose an SVM-based quasi-statistical approach to multi-modal pattern recognition which covers both of these modes of kernel fusion.

Keywords

Kernel-based pattern recognition support vector machines combining modalities kernel fusion 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Vadim Mottl
    • 1
  • Alexander Tatarchuk
    • 1
  • Valentina Sulimova
    • 1
  • Olga Krasotkina
    • 1
  • Oleg Seredin
    • 2
  1. 1.Computing Center of the Russian Academy of Sciences, Vavilov St., 40, 117968 MoscowRussia
  2. 2.Tula State University, Lenin Ave. 92, 300600 TulaRussia

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