Salient Object Detection via Nonlocal Diffusion Tensor

Xiujun Zhang, Chen Xu, Xiaoli Sun, George Baciu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


In this paper, visual attention spreading is formulated as a nonlocal diffusion equation. Different from other diffusion-based methods, a nonlocal diffusion tensor is introduced to consider both the diffusion strength and the diffusion direction. With the help of diffusion tensor, along with the principle direction, the diffusion has been suppressed to preserve the dissimilarity between the foreground and background, while in other directions, the diffusion has been boosted to combine the similar regions and highlight the salient object as a whole. Through a two-stages diffusion, the final saliency maps are obtained. Extensive quantitative or visual comparisons are performed on three widely used benchmark datasets, i.e. MSRA-ASD, MSRA-B and PASCAL-1500 datasets. Experimental results demonstrate the superior performance of our method.
Original languageEnglish
Article number1555013
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number7
Publication statusPublished - 1 Jan 2015


  • diffusion equation
  • diffusion tensor
  • nonlocal operator
  • Salient object detection

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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