Learning Boundary and Appearance for Video Object Cutout

This letter presents an approach to object cutout for arbitrary videos. Our approach is in a “learning and propagating” way. First, the object(s) of interest is (are) cut out in the first frame (or the key frame) in the video clip using some interactive image segmentation tool. Then some statistical features of the object regions, the non-object regions, and the boundaries are learnt from the result of image segmentation. Finally, the result is propagated to the other frames automatically. We formulate the “learning and propagating” step in Markov random fields (MRFs). In this model, we well design the patch term and the boundary term to significantly improve the performance of the algorithm. Experimental results indicate that the algorithm performs excellent.