Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 33, Issue 2, Pages 353-367Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2010.70
Keywords
Salient object detection; conditional random field; visual attention; saliency map
Funding
- National Natural Science Foundation of China [90820017]
- National Basic Research Program of China [2007CB311005]
- National High-Tech Research and Development Plan of China [2006AA01Z192]
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In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. Further, we extend the proposed approach to detect a salient object from sequential images by introducing the dynamic salient features. We collected a large image database containing tens of thousands of carefully labeled images by multiple users and a video segment database, and conducted a set of experiments over them to demonstrate the effectiveness of the proposed approach.
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