Comparison of combining methods of correlation Kernels in kPCA and kCCA for texture classification with Kansei information

Authors:
Yo Horikawa;Yujiro Ohnishi
Affiliations:
Faculty of Engineering, Kagawa University, Takamatsu, Japan;Ryobi Systems Corporation, Okayama, Japan
Venue:
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Year:
2007

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Abstract

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.