Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability

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...

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Bibliographic Details
Published in:IEEE transactions on image processing 2018-02, Vol.27 (2), p.987-998
Main Authors: Zhang, Lihe, Ai, Jianwu, Jiang, Bowen, Lu, Huchuan, Li, Xiukui
Format: Article
Language:English
Subjects:
absorbing Markov chain
angular embedding
Boundary maps
Coding
Computational modeling
Feature extraction
Hierarchies
Image segmentation
Markov analysis
Markov chains
Markov processes
Object detection
Salience
Salient object detection
Sparse matrices
Transient analysis
transition probability matrix
Transportation networks
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2766787