Support vector machines for texture classification

Kim, Kwang In and Jung, Keechul and Park, Se Hyun and Kim, Hang Joon (2002) Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (11). pp. 1542-1550. ISSN 0162-8828

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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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncontrolled Keywords:
?? artificial intelligencecomputational theory and mathematicssoftwareapplied mathematicscomputer vision and pattern recognition ??
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Deposited On:
03 Jul 2014 08:51
Last Modified:
31 Dec 2023 00:31