期刊
IEEE SIGNAL PROCESSING LETTERS
卷 22, 期 11, 页码 2073-2077出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2015.2458434
关键词
Co-saliency detection; exemplar; object discovery; object recovery; saliency model
资金
- National Natural Science Foundation of China [61171144, 61471230]
- Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning
This letter proposes a novel co-saliency model to effectively discover and highlight co-salient objects in a set of images. Based on the gross similarity which combines color features and SIFT descriptors, some co-salient object regions are first discovered in each image as exemplars, which are exploited to generate the exemplar saliency maps with the use of single-image saliency model. Then both local recovery and global recovery of co-salient object regions are performed by propagating the exemplar saliency to the matched regions, and border connectivity is further exploited to generate the region-level co-saliency maps. Finally, the foci of attention area based pixel-level saliency derivation is used to generate the pixel-level co-saliency maps with even better quality. Experimental results on two benchmark datasets demonstrate that the proposed co-saliency model outperforms the state-of-the-art co-saliency models.
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