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论文类型：会议论文

第一作者：Wang,Xinggang,Wang,Xinggang

通讯作者：Wang,Xinggang

合写作者：Latecki,Jan,Longin,Liu,Wenyu,Yang,Xingwei,Bai,Xiang

发表刊物：Advances in Neural Information Processing Systems 24 (NIPS 2011)

发表时间：2011-12-12

摘要：We propose a novel inference framework for finding maximal cliques in a weighted graph that satisfy hard constraints. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., sets of nodes that cannot belong to the same solution. The proposed inference is based on a novel particle filter algorithm with state permeations. We apply the inference framework to a challenging problem of learning part-based, deformable object models. Two core problems in the learning framework, matching of image patches and finding salient parts, are formulated as two instances of the problem of finding maximal cliques with hard constraints. Our learning framework yields discriminative part based object models that achieve very good detection rate, and outperform other methods on object classes with large deformation.