MAP Inference with MRF by Graduated Non-Convexity and Concavity Procedure
Zhi-Yong Liu; Hong Qiao; Jian-Hua Su
2014
Conference NameNeural Information Processing 21st International Conference, ICONIP 2014
Source PublicationNeural Information Processing. 21st International Conference, ICONIP 2014. Proceedings: LNCS 8835
Conference Date3-6 Nov. 2014
Conference PlaceKuching, Malaysia
AbstractIn 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.
KeywordNone
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12866
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorZhi-Yong Liu
Recommended Citation
GB/T 7714
Zhi-Yong Liu,Hong Qiao,Jian-Hua Su. MAP Inference with MRF by Graduated Non-Convexity and Concavity Procedure[C],2014.
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