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Self-Validated Labeling of Markov Random Fields for Image Segmentation
2010
IEEE Transactions on Pattern Analysis and Machine Intelligence
This paper addresses the problem of self-validated labeling of Markov random fields (MRFs), namely to optimize an MRF with unknown number of labels. We present graduated graph cuts (GGC), a new technique that extends the binary s-t graph cut for self-validated labeling. Specifically, we use the split-and-merge strategy to decompose the complex problem to a series of tractable subproblems. In terms of Gibbs energy minimization, a suboptimal labeling is gradually obtained based upon a set of
doi:10.1109/tpami.2010.24
pmid:20724763
fatcat:idje2n5przcbtftn7dyxjbinma