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Geodesic Saliency Using Background Priors

  • Yichen Wei
  • Fang Wen
  • Wangjiang Zhu
  • Jian Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

Abstract

Generic object level saliency detection is important for many vision tasks. Previous approaches are mostly built on the prior that “appearance contrast between objects and backgrounds is high”. Although various computational models have been developed, the problem remains challenging and huge behavioral discrepancies between previous approaches can be observed. This suggest that the problem may still be highly ill-posed by using this prior only.

In this work, we tackle the problem from a different viewpoint: we focus more on the background instead of the object. We exploit two common priors about backgrounds in natural images, namely boundary and connectivity priors, to provide more clues for the problem. Accordingly, we propose a novel saliency measure called geodesic saliency. It is intuitive, easy to interpret and allows fast implementation. Furthermore, it is complementary to previous approaches, because it benefits more from background priors while previous approaches do not.

Evaluation on two databases validates that geodesic saliency achieves superior results and outperforms previous approaches by a large margin, in both accuracy and speed (2 ms per image). This illustrates that appropriate prior exploitation is helpful for the ill-posed saliency detection problem.

Keywords

Image Patch Salient Object Saliency Detection Image Boundary Salient Object Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yichen Wei
    • 1
  • Fang Wen
    • 1
  • Wangjiang Zhu
    • 1
  • Jian Sun
    • 1
  1. 1.Microsoft Research AsiaChina

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