MAP Inference with MRF by Graduated Non-Convexity and Concavity Procedure

	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	多模态人工智能系统全国重点实验室_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.