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Green Image
Issue No. 02 - February (2011 vol. 33)
ISSN: 0162-8828
pp: 353-367
Nanning Zheng , Xi'an Jiaotong Uinversity, Xi'an
Xiaoou Tang , Chinese University of Hong Kong, Hong Kong
Heung-Yeung Shum , Microsoft, Redmond
Jingdong Wang , Microsoft Research Aisa, Beijing
Tie Liu , Xi'an Jiaotong University, Xi'an and IBM Research-China, Beijing
Jian Sun , Microsoft Research Asia, Beijing
Zejian Yuan , Xi'an Jiaotong Uinversity, Xi'an
ABSTRACT
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.
INDEX TERMS
Salient object detection, conditional random field, visual attention, saliency map.
CITATION
Nanning Zheng, Xiaoou Tang, Heung-Yeung Shum, Jingdong Wang, Tie Liu, Jian Sun, Zejian Yuan, "Learning to Detect a Salient Object", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 353-367, February 2011, doi:10.1109/TPAMI.2010.70
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