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Support Vector Machines for Texture Classification
November 2002 (vol. 24 no. 11)
pp. 1542-1550

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.

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Index Terms:
Support vector machines, texture analysis, pattern classification, machine learning, feature extraction.
Kwang In Kim, Keechul Jung, Se Hyun Park, Hang Joon Kim, "Support Vector Machines for Texture Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1542-1550, Nov. 2002, doi:10.1109/TPAMI.2002.1046177
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