Computer Science ›› 2018, Vol. 45 ›› Issue (10): 272-275.doi: 10.11896/j.issn.1002-137X.2018.10.050

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Saliency Detection Based on Surroundedness and Markov Model

CHEN Bing-cai1,2, WANG Xi-bao1, YU Chao1, NIAN Mei2, TAO Xin1, PAN Wei-min2, LU Zhi-mao1   

  1. Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China 1
    College of Computer Science and Technology,Xinjiang Normal University,Urumqi 830054,China 2
  • Received:2017-09-05 Online:2018-11-05 Published:2018-11-05

Abstract: Aiming at solving the problem of saliency detection,this paper proposed a saliency detection algorithm based on surrouuundedness and markov model.Firstly,the surroundedness is used to predict the approximate region of the salient object for eye fixation.Secondly,a simple linear iterative clustering (SLIC) algorithm is used to process the ori-ginal image,and the graph model of the image is established based on the superpixels.Next,the superpixels of the two boundaries that are the furthest from the approximate region of the salient object are taken as the virtual background absorb nodes,the saliency value of each superpixel is calculated by the absorption Markov chain,and the initial saliency map S1 is detected.Then,superpixels in the approximate region of the salient object is used as the virtual foreground absorption nodes,and the initial saliency map S2 is detected by the absorption Markov chain.Then S1 and S2 are fused to get the final saliency map S.Finally,the guided filter is used to smooth the saliency maps and get a better saliency map.Experimental results based on two public datasets demonstrate that the proposed algorithm outperforms many state-of-the-art methods.

Key words: Saliency object detection, Markov model, Surroundedness, Background prior, Foreground prior

CLC Number: 

  • TP391
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