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IEEE Trans Image Process. 2013 Oct;22(10):3766-78. doi: 10.1109/TIP.2013.2260166. Epub 2013 Apr 25.

Cluster-based co-saliency detection.

Author information

  • 1School of Computer Science and Technology, Tianjin University, Tianjin, China. hzfu@tju.edu.cn

Abstract

Co-saliency is used to discover the common saliency on the multiple images, which is a relatively underexplored area. In this paper, we introduce a new cluster-based algorithm for co-saliency detection. Global correspondence between the multiple images is implicitly learned during the clustering process. Three visual attention cues: contrast, spatial, and corresponding, are devised to effectively measure the cluster saliency. The final co-saliency maps are generated by fusing the single image saliency and multiimage saliency. The advantage of our method is mostly bottom-up without heavy learning, and has the property of being simple, general, efficient, and effective. Quantitative and qualitative experiments result in a variety of benchmark datasets demonstrating the advantages of the proposed method over the competing co-saliency methods. Our method on single image also outperforms most the state-of-the-art saliency detection methods. Furthermore, we apply the co-saliency method on four vision applications: co-segmentation, robust image distance, weakly supervised learning, and video foreground detection, which demonstrate the potential usages of the co-saliency map.

PMID:
23629857
[PubMed]
PMCID:
PMC3785793
[Available on 2014/10/1]
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