Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 146-150.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Salient Object Detection Based on Dictionary and Weighted Low-rank Recovery

MA Xiao-di, WU Xi-yin, JIN Zhong   

  1. School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
    Key Laboratory of Intelligent Perception and System for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology,Nanjing 210094,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: Salient object detection intends to identify salient areas in natural images.In order to improve detection results,a method based on dictionary and weighted low-rank recovery for salient object detection was proposed.Firstly,a dictionary is incorporated into the low rank recovery model to separate the low rank matrix from the sparse matrix better.Secondly,sparse matrices corresponding to the color,location and boundary connectivity priors are obtained,and the adaptive coefficients are generated by their saliency values.Finally,a weighted matrix is constructed by adaptive coefficients with three priors,and the matrix is merged into the low rank recovery model.Compared with eleven state-of-the-art methods in four challenging databases,the experiment results show that the proposed approach outperforms the state-of-the-art solutions.

Key words: Adaptive coefficient, Background prior, Dictionary, Weighted low-rank recovery

CLC Number: 

  • TP391
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