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Geodesic Saliency Using Background Priors
| Citations: | 32 - 1 self |
BibTeX
@MISC{Wei_geodesicsaliency,
author = {Yichen Wei and Fang Wen and Wangjiang Zhu and Jian Sun},
title = {Geodesic Saliency Using Background Priors},
year = {}
}
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Abstract
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. 1
Keyphrases
previous approach geodesic saliency using background prior geodesic saliency achieves superior result fast implementation geodesic saliency background prior huge behavioral discrepancy different viewpoint generic object level saliency detection natural image many vision task ill-posed saliency detection problem novel saliency measure large margin common prior appropriate prior exploitation connectivity prior various computational model appearance contrast
