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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 2138747, 18 pages
http://dx.doi.org/10.1155/2016/2138747
Research Article

Salient Region Detection by Fusing Foreground and Background Cues Extracted from Single Image

1Department of Electronics & Information, Tongji University, Shanghai, China
2Department of Software Engineering, Tongji University, Shanghai, China

Received 17 May 2016; Revised 21 August 2016; Accepted 29 August 2016

Academic Editor: Erik Cuevas

Copyright © 2016 Qiangqiang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Saliency detection is an important preprocessing step in many application fields such as computer vision, robotics, and graphics to reduce computational cost by focusing on significant positions and neglecting the nonsignificant in the scene. Different from most previous methods which mainly utilize the contrast of low-level features, various feature maps are fused in a simple linear weighting form. In this paper, we propose a novel salient object detection algorithm which takes both background and foreground cues into consideration and integrate a bottom-up coarse salient regions extraction and a top-down background measure via boundary labels propagation into a unified optimization framework to acquire a refined saliency detection result. Wherein the coarse saliency map is also fused by three components, the first is local contrast map which is in more accordance with the psychological law, the second is global frequency prior map, and the third is global color distribution map. During the formation of background map, first we construct an affinity matrix and select some nodes which lie on border as labels to represent the background and then carry out a propagation to generate the regional background map. The evaluation of the proposed model has been implemented on four datasets. As demonstrated in the experiments, our proposed method outperforms most existing saliency detection models with a robust performance.