Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.