李秀魁

个人信息Personal Information

副教授

硕士生导师

性别:男

毕业院校:密西根理工大学

学位:博士

所在单位:信息与通信工程学院

学科:信号与信息处理

办公地点:海山楼B0310

联系方式:86-411-84706002, Ext. 2503

电子邮箱:xli@dlut.edu.cn

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Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability

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论文类型:期刊论文

发表时间:2018-02-01

发表刊物:IEEE TRANSACTIONS ON IMAGE PROCESSING

收录刊物:SCIE、EI、Scopus

卷号:27

期号:2

页面范围:987-998

ISSN号:1057-7149

关键字:Salient object detection; absorbing Markov chain; transition probability matrix; angular embedding

摘要:In this paper, we propose a bottom-up saliency model based on absorbing Markov chain (AMC). First, a sparsely connected graph is constructed to capture the local context information of each node. All image boundary nodes and other nodes are, respectively, treated as the absorbing nodes and transient nodes in the absorbing Markov chain. Then, the expected number of times from each transient node to all other transient nodes can be used to represent the saliency value of this node. The absorbed time depends on the weights on the path and their spatial coordinates, which are completely encoded in the transition probability matrix. Considering the importance of this matrix, we adopt different hierarchies of deep features extracted from fully convolutional networks and learn a transition probability matrix, which is called learnt transition probability matrix. Although the performance is significantly promoted, salient objects are not uniformly highlighted very well. To solve this problem, an angular embedding technique is investigated to refine the saliency results. Based on pairwise local orderings, which are produced by the saliency maps of AMC and boundary maps, we rearrange the global orderings (saliency value) of all nodes. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art methods on six publicly available benchmark data sets.