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Dynamic Similarity Kernel for Visual Recognition

  • Wang Yan
  • Qingshan Liu
  • Hanqing Lu
  • Songde Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)

Abstract

Inspired by studies of cognitive psychology, we proposed a new dynamic similarity kernel for visual recognition. This kernel has great consistency with human visual similarity judgement by incorporating the perceptual distance function. Moreover, this kernel can be seen as an extension of Gaussian kernel, and therefore can deal with nonlinear variations well like the traditional kernels. Experimental results on natural image classification and face recognition show its superior performance compared to other kernels.

Keywords

Face Recognition Linear Discriminant Analysis Gaussian Kernel Image Retrieval Polynomial Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wang Yan
    • 1
  • Qingshan Liu
    • 1
  • Hanqing Lu
    • 1
  • Songde Ma
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingP.R. China

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