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Texture classification using the support vector machines

✍ Scribed by Shutao Li; James T. Kwok; Hailong Zhu; Yaonan Wang

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
515 KB
Volume
36
Category
Article
ISSN
0031-3203

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✦ Synopsis

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 classiΓΏcation, 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 di erent setting of the kernel parameter. Compared to the traditional Bayes classiΓΏer and the learning vector quantization algorithm, SVMs, and, in particular, the fused output from multiple SVMs, produce more accurate classiΓΏcation results on the Brodatz texture album.

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