Stochastic image segmentation by combining region and edge cues

In this paper, we present a probabilistic framework for edge and region grouping using conditional random field. Our model is built on a hybrid adjacency graph of atomic region and contour primitives. Unary and pairwise potentials that capture similarity, proximity and curvilinear continuity are defined. Similarity, for both region and edge cues, is measured by likelihood ratios learned from a human labeled ground truth. We use a stochastic graph partition algorithm, Swendsen-Wang Cut, to perform inference on this model. Experimental results are shown on gray-scale natural images.