Abstract

Generic ob ject level saliency detection is important for many vision tasks. Previous approaches are mostly built on the prior that “ap- pearance contrast between ob jects and backgrounds is high”. Although various computational models have been developed, the problem re- mains 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 ob ject. We exploit two common priors about backgrounds in natural images, namely boundary and connectivity priors, to provide more clues for the problem. Accord- ingly, we propose a novel saliency measure called geodesic saliency. It is intuitive, easy to interpret and allows fast implementation. Further- more, 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 ap- propriate prior exploitation is helpful for the ill-posed saliency detection problem.