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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