Image matting based on local color discrimination by SVM

Image matting is a technique used for extracting a foreground object in a static image by estimating the opacity, called alpha matte, at each pixel in the foreground image layer. The common drawback of the previous matting approaches is the decrease in performance when a foreground and its background have similar colors. In order to overcome this problem, we propose a method of estimating alpha mattes by using the color information of neighboring pixels and the support vector machine. We define a cost function on the basis of a Markov random field by considering not only a single pixel but also its neighboring pixels and utilizing the support vector machine to enhance the discrimination between the foreground and the background. This cost function is minimized by the belief propagation and the sampling methods. Qualitative and quantitative results have shown a favorable matting performance compared to the other methods.