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Learning to Detect a Salient Object

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7 Author(s)
Tie Liu ; Analytics & Optimization Dept., Xi''an Jiaotong Univ., Beijing, China ; Zejian Yuan ; Jian Sun ; Jingdong Wang
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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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:33 ,  Issue: 2 )

Date of Publication:

Feb. 2011

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