Support vector machines for texture classification

Top Cited Papers

1 November 2002

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 24 (11) , 1542-1550
https://doi.org/10.1109/tpami.2002.1046177

Abstract

This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods. One-against-others decomposition is adopted to apply binary SVMs to multitexture classification, plus a neural network is used as an arbitrator to make final classifications from several one-against-others SVM outputs. Experimental results demonstrate the effectiveness of SVMs in texture classification.

Keywords

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