Texture classification using the support vector machines

Shutao Li, James T. Kwok, Hailong Zhu, Yaonan Wang

Abstract: In recent years, support vector machines (SVMs) have demonstrated excellent performance in a variety of pattern recognition problems. In this paper, we apply SVMs for texture classification, using translation-invariant features generated from the discrete wavelet frame transform. To alleviate the problem of selecting the right kernel parameter in the SVM, we use a fusion scheme based on multiple SVMs, each with a different setting of the kernel parameter. Compared to the traditional Bayes classifier and the learning vector quantization algorithm, SVMs, and, in particular, the fused output from multiple SVMs, produce more accurate classification results on the Brodatz texture album.

Pattern Recognition, 36(12):2883-2893, Dec 2003.

Postscript: http://www.cs.ust.hk/~jamesk/papers/pr03.ps.gz

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