Details

Autor(en) / Beteiligte

• Fu, Huazhu
• Cao, Xiaochun
• Tu, Zhuowen

Titel

Cluster-Based Co-Saliency Detection

Ist Teil von

• IEEE transactions on image processing, 2013-10, Vol.22 (10), p.3766-3778

Ort / Verlag

New York, NY: IEEE

Erscheinungsjahr

2013

Link zum Volltext

• Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.