title: Learning to detect a salient object.
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.
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Beijing, China. liultie@cn.ibm.com
authors.forename: Tie
authors.initials: T
authors.lastname: Liu
authors.forename: Zejian
authors.initials: Z
authors.lastname: Yuan
authors.forename: Jian
authors.initials: J
authors.lastname: Sun
authors.forename: Jingdong
authors.initials: J
authors.lastname: Wang
authors.forename: Nanning
authors.initials: N
authors.lastname: Zheng
authors.forename: Xiaoou
authors.initials: X
authors.lastname: Tang
authors.forename: Heung-Yeung
authors.initials: HY
authors.lastname: Shum