Support vector machines for texture classification

Kwang In Kim, Keechul Jung, Se Hyun Park, Hang Joon Kim

Research output: Contribution to journalArticle

294 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1542-1550
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number11
Publication statusPublished - Nov 2002

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