Computer Science ›› 2018, Vol. 45 ›› Issue (8): 258-263.doi: 10.11896/j.issn.1002-137X.2018.08.046

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

Salient Object Detection Algorithm Based on Sparse Recovery and Optimization

WANG Jun1, WU Ze-min1, YANG Wei2, HU Lei1, ZHANG Zhao-feng3, JIANG Qing-zhu4   

  1. College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China1
    No.722 Research Institute,China Shipbuilding Industry Corporation,Wuhan 430079,China2
    The 61428th Troops of the PLA,Beijing 100071,China3
    The 95980th Troops of the PLA,Xiangyang,Hubei 442101,China4
  • Received:2017-10-19 Online:2018-08-29 Published:2018-08-29

Abstract: In view of the issues of boundary ambiguity and low detection accuracy in current saliency detection algorithms which employ sparse representation,this paper proposed a new saliency detection algorithm based on sparse recovery and optimization.Firstly,the RG filter is used to smooth the image.Then,the SLIC algorithm is used to segment the image,and the reliable background seed is selected from the boundary and the inside super pixel block is chosen to construct the dictionary.Based on the dictionary,the sparse recovery of the whole image is achieved,and the initial sa-liency map is generated according to the sparse recovery error.After that,the modified optimization model is used to optimize the initial saliency map.Finally,the final saliency map is obtained through multiscale fusion.Experimental results on three public benchmark datasets show that the performance of the proposed algorithm is superior to the current state-of-the-art methods.Meanwhile,it performs well in dealing with boundary saliency and has strong robustness.

Key words: Saliency detection, Sparse recovery, Saliency optimization

CLC Number: 

  • TP393
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