Arbitrary body segmentation in static images

In this paper, a novel method for segmenting arbitrary human body in static images is proposed. With the body probability map obtained by the pictorial structure model, we develop a superpixel based EM-like algorithm to refine the map, which can then serve as the seeds of graph cuts optimization. To better obtain the final segmentation, we propose a novel @?"1 based graph cuts algorithm, which uses the sparse coding to construct the initialized graph and calculates the terminal links (t-links) and neighborhood links (n-links) simultaneously from the constructed graph. By employing this @?"1 based graph cuts, we can effectively and efficiently segment the human body from static images. The experiments on the publicly available challenging datasets demonstrate that our method outperforms many state-of-the-art methods on human body segmentation.