Supervised texture segmentation using support vectormachines

An approach to the problem of supervised texture segmentation using nonlinear support vector machines (SVMs) is presented. For each texture class a nonlinear SVM is constructed which separates that class from the other classes. The segmentation then works by applying all the SVMs to an input image and arbitrating between the SVM outputs. Experimental results show the effectiveness of the proposed method.