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Support vector machines for texture classification

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4 Author(s)
Kwang In Kim ; Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea ; Keechul Jung ; Se Hyun Park ; Hang Joon Kim

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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:24 ,  Issue: 11 )