Learning to Detect a Salient Object

Authors:
Tie Liu;Zejian Yuan;Jian Sun;Jingdong Wang;Nanning Zheng;Xiaoou Tang;Heung-Yeung Shum
Affiliations:
Xi'an Jiaotong University, Xi'an and IBM Research-China, Beijing;Xi'an Jiaotong Uinversity, Xi'an;Microsoft Research Asia, Beijing;Microsoft Research Aisa, Beijing;Xi'an Jiaotong Uinversity, Xi'an;Chinese University of Hong Kong, Hong Kong;Microsoft, Redmond
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2011

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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.