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17th International Conference on Pattern Recognition (ICPR'04) - Volume 1
Comparison of Support Vector Machines with Autocorrelation Kernels for Invariant Texture Classification
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
Yo Horikawa, Kagawa University, Japan
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.
Citation:
Yo Horikawa, "Comparison of Support Vector Machines with Autocorrelation Kernels for Invariant Texture Classification," icpr, vol. 1, pp.660-663, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004
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