Arbitrary body segmentation in static images

Authors:
Shifeng Li;Huchuan Lu;Lei Zhang
Affiliations:
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116023, China;School of Information and Communication Engineering, Dalian University of Technology, Dalian 116023, China;Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Venue:
Pattern Recognition
Year:
2012

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Abstract

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.