MRF Matting on Complex Images

This paper proposes a novel approach to solve the matting problem on complex images using Markov Random Field(MRF) model. Although many natural image matting methods have been proposed, matting on complex images still remains as a challenge. Our approach, which we call MRF matting, partitions the image manually into three regions: foreground, background, and unknown region. Then, the unknown region is roughly segmented into several joint sub-regions by the user. In each sub-region, matting labels are defined and modelled as an MRF and assigned to the pixels in unknown region. Matting problem is then formulated as a maximum a posteriori(MAP) estimation problem on this MRF model and its associated Gibbs distribution. Simulated annealing is used to find the optimal matting solution. We compute alpha mattes of all the sub-regions and combine them into a final matte. When matting on complex images, our approach is demonstrated to be more robust than existing methods. Experimental results are shown and compared with other methods in this paper.