Salient Object Detection: A Discriminative Regional FeatureIntegration Approach
- Jingdong Wang ,
- Huaizu Jiang ,
- Zejian Yuan ,
- Ming-Ming Cheng ,
- Xiaowei Hu ,
- Nanning Zheng
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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 integra-
tion, which learns a random forest regressor to discrimina-
tively integrate the saliency features for saliency computa-
tion. In addition to contrast features, we introduce regional
object-sensitive descriptors: the objectness descriptor char–
acterizing 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 success-
ful 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.