Comparison of Combining Methods of Correlation Kernels in kPCA and kCCA for Texture Classification with Kansei Information

  • Yo Horikawa
  • Yujiro Ohnishi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


The authors consider combining correlations of different orders in kernel principal component analysis (kPCA) and kernel canonical correlation analysis (kCCA) with the correlation kernels. We apply combining methods, e.g., the sums of the correlation kernels, Cartesian spaces of the principal components or the canonical variates and the voting of kPCAs and kCCAs output and compare their performance in the classification of texture images. Further, we apply Kansei information on the images obtained through questionnaires to the public to kCCA and evaluate its effectiveness.


kernel method principal component analysis canonical correlation analysis correlation kernel combining classifiers Kansei information texture classification 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yo Horikawa
    • 1
  • Yujiro Ohnishi
    • 2
  1. 1.Faculty of Engineering, Kagawa University, Takamatsu, 761-0396Japan
  2. 2.Ryobi Systems Corporation, Okayama, 700-8504Japan

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