MAP Inference with MRF by Graduated Non-Convexity and Concavity Procedure
Zhi-Yong Liu; Hong Qiao; Jian-Hua Su
2014
会议名称Neural Information Processing 21st International Conference, ICONIP 2014
会议录名称Neural Information Processing. 21st International Conference, ICONIP 2014. Proceedings: LNCS 8835
会议日期3-6 Nov. 2014
会议地点Kuching, Malaysia
摘要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.
关键词None
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12866
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
通讯作者Zhi-Yong Liu
推荐引用方式
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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