期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 22, 期 10, 页码 3766-3778出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2013.2260166
关键词
Saliency detection; co-saliency; co-segmentation; weakly supervised learning
资金
- National Basic Research Program of China [2013CB329305]
- National High-Tech R&D Program of China [2012AA011503]
- 100 Talents Programme of The Chinese Academy of Sciences
- Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology
- Tianjin Key Technologies RD Program [11ZCKFGX00800]
- NSF [IIS-0844566, IIS-1216528]
- NIH [R01 MH094343]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1360568] Funding Source: National Science Foundation
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.
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