Image Segmentation Using MAP-MRF Estimation and Support Vector Machine

Image segmentation has recently been studied in a framework of maximum a posteriori estimation for the Markov random field, where the cost function representing pixel-wise likelihood and inter-pixel smoothness should be minimized. The common drawback of these studies is the decrease in performance when a foreground object and the background have similar colors. We propose the likelihood formulation in the cost function considering not only a single pixel but also its neighboring pixels, and utilizing the support vector machine to enhance the discrimination between foreground and background. The global optimal solution for our cost function can be realized by the graph cut algorithm. Experimental results show an excellent segmentation performance in many cases.