Comparison of Support Vector Machines with Autocorrelation Kernels for Invariant Texture Classification

Authors:
Yo Horikawa
Affiliations:
Kagawa University, Japan
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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

SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis

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Abstract

Support vector machines (SVMs) with autocorrelation kernels are applied to texture classification invariant to similarity transformations and noise. The inner product of autocorrelation functions of an arbitrary order is effectively calculated through the 2nd-order crosscorrelation of original data. Texture classification experiments show that higher performance of SVMs is achieved by exploiting the autocorrelation kernels.