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Multimedia Systems

, Volume 22, Issue 2, pp 245–253 | Cite as

Saliency detection using boundary information

  • Beiji Zou
  • Qing Liu
  • Zailiang Chen
  • Shijian Liu
  • Xiaoyun Zhang
Regular Paper

Abstract

Efficient and robust saliency detection is a fundamental problem in computer vision field for its wide applications, such as image segmentation and image retargeting, etc. In this paper, with the aim of uniformly highlighting the salient objects and suppressing the saliency of the background in images, we propose an efficient three-stage saliency detection method. First, boundary prior and connectivity prior are used to generate coarse saliency maps. To suppress the saliency value of the cluttered background, two supergraphs together with the adjacent graph are created so that the saliency of the background regions with similar appearances which are separated by other regions can be reduced effectively. Second, a local context-based saliency propagation is proposed to refine the saliency such that regions with similar features hold similar saliency. Finally, a logistic regressor is learned to combine the three refined saliency maps into the final saliency map automatically. The proposed method improves saliency detection on many cluttered images. The experimental results on two widely used public datasets with pixel accurate salient region annotations show that our method outperforms the state-of-the-art methods.

Keywords

Saliency detection Boundary information Saliency propagation 

Notes

Acknowledgments

We would like to thank the anonymous reviewers for their comments. This research was supported in part by the National Natural Science Foundation of China under Grant No. 61173122, No. 61262032 and No. 61440055 and the Fundamental Research Funds for the Central Universities of Central South University under Grant No. 2013zzts046.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Beiji Zou
    • 1
  • Qing Liu
    • 1
  • Zailiang Chen
    • 1
  • Shijian Liu
    • 1
  • Xiaoyun Zhang
    • 1
  1. 1.School of Information Science and Engineering Central South UniversityChangshaPeople’s Republic of China

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