Bio-inspired Visual Attention Model and Saliency Guided Object Segmentation

  • Lijuan Duan
  • Jili Gu
  • Zhen Yang
  • Jun Miao
  • Wei Ma
  • Chunpeng Wu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 238)


In this paper, we present a saliency guided image object segment method. We suppose that saliency maps can indicate informative regions, and filter out background in images. To produce perceptual satisfactory salient objects, we use our bio-inspired saliency measure which integrating three factors: dissimilarity, spatial distance and central bias to compute saliency map. Then the saliency map is used as the importance map in the salient object segment method. Experimental results demonstrate that our method outperforms previous saliency detection method, yielding higher precision (0.7669) and better recall rates (0.825), F-Measure (0.7545), when evaluated using one of the largest publicly available data sets.


visual attention dissimilarity spatial distance central bias salient object detection 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lijuan Duan
    • 1
  • Jili Gu
    • 1
  • Zhen Yang
    • 1
  • Jun Miao
    • 2
  • Wei Ma
    • 1
  • Chunpeng Wu
    • 3
  1. 1.College of Computer Science and TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Key Laboratory of Intelligent Information ProcessingInstitute of Computing Technology, Chinese Academy of SciencesBeijingChina
  3. 3.Fujitsu Research & Development Center Co. Ltd.BeijingChina

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