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Published in:
2014 | OriginalPaper | Chapter
MAP Inference with MRF by Graduated Non-Convexity and Concavity Procedure
Authors : Zhi-Yong Liu, Hong Qiao, Jian-Hua Su
Published in: Neural Information Processing
Publisher: Springer International Publishing
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In this paper we generalize the recently proposed graduated non-convexity and concavity procedure (GNCCP) to approximately solve the maximum a posteriori (MAP) inference problem with the Markov random field (MRF). Unlike the commonly used graph cuts or loopy brief propagation, the GNCCP based MAP algorithm is widely applicable to any types of graphical models with any types of potentials, and is very easy to use in practice. Our preliminary experimental comparisons witness its state-of-the-art performance.