The Visual Computer

, Volume 33, Issue 11, pp 1415–1428 | Cite as

Salient object detection in complex scenes via D-S evidence theory based region classification

  • Chunlei Yang
  • Jiexin PuEmail author
  • Yongsheng Dong
  • Zhonghua Liu
  • Lingfei Liang
  • Xiaohong Wang
Original Article


In complex scenes, multiple objects are often concealed in cluttered backgrounds. Their saliency is difficult to be detected by using conventional methods, mainly because single color contrast can not shoulder the mission of saliency measure; other image features should be involved in saliency detection to obtain more accurate results. Using Dempster-Shafer (D-S) evidence theory based region classification, a novel method is presented in this paper. In the proposed framework, depth feature information extracted from a coarse map is employed to generate initial feature evidences which indicate the probabilities of regions belonging to foreground or background. Based on the D-S evidence theory, both uncertainty and imprecision are modeled, and the conflicts between different feature evidences are properly resolved. Moreover, the method can automatically determine the mass functions of the two-stage evidence fusion for region classification. According to the classification result and region relevance, a more precise saliency map can then be generated by manifold ranking. To further improve the detection results, a guided filter is utilized to optimize the saliency map. Both qualitative and quantitative evaluations on three publicly challenging benchmark datasets demonstrate that the proposed method outperforms the contrast state-of-the-art methods, especially for detection in complex scenes.


Salient object detection Complex scene D-S evidence theory Multiple feature fusion Region classification 



This work was supported in part by the International S & T Cooperation Program of China (No. 2011DFR10480), the Natural Science Foundation of China (No. 61301230), and the Key Project of Science and Technology of Henan (No. 142107000021).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Henan University of Science and TechnologyLuoyangChina

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