A kernel method based chance constrained support vector machine with a slack term (KSCC-SVM) is investigated and applied into image segmentation in this paper. The modified model aims at mining nonlinear separable data with noises. It is resolved based on chance constrained programming. Both soft classification and kernel method are involved in this approach for a better segmentation result. In the image pre-processing stage, fast generalized fuzzy c-means (FGFCM) algorithm is utilized for labeling samples. Then these labeled samples are randomly selected to train KSCC-SVM. Experimental results show the efficiency and robustness of the new proposed algorithm.

support vector machine, chance constrained programming, image segmentation, second order cone programming, fuzzy c-means.