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.
|Number of pages||9|
|Journal||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|Publication status||Published - Nov 2002|
Kim, K. I., Jung, K., Park, S. H., & Kim, H. J. (2002). Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11), 1542-1550. https://doi.org/10.1109/TPAMI.2002.1046177