Journal
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume 123, Issue 2, Pages 251-268Publisher
SPRINGER
DOI: 10.1007/s11263-016-0977-3
Keywords
Salient object detection; Data-driven
Categories
Funding
- National Basic Research Program of China [2015CB351703, 2012CB316400]
- National Natural Science Foundation of China [91120006]
- NSFC [61572264]
- Huawei Innovation Research Program (HIRP)
- CAST young talents plan
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Feature integration provides a computational framework for saliency detection, and a lot of hand-crafted integration rules have been developed. In this paper, we present a principled extension, supervised feature integration, which learns a random forest regressor to discriminatively integrate the saliency features for saliency computation. In addition to contrast features, we introduce regional object-sensitive descriptors: the objectness descriptor characterizing the common spatial and appearance property of the salient object, and the image-specific backgroundness descriptor characterizing the appearance of the background of a specific image, which are shown more important for estimating the saliency. To the best of our knowledge, our supervised feature integration framework is the first successful approach to perform the integration over the saliency features for salient object detection, and outperforms the integration approach over the saliency maps. Together with fusing the multi-level regional saliency maps to impose the spatial saliency consistency, our approach significantly outperforms state-of-the-art methods on seven benchmark datasets. We also discuss several followup works which jointly learn the representation and the saliency map using deep learning.
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