Image co-saliency detection via locally adaptive saliency map fusion

Citations of this article
Mendeley users who have this article in their library.
Get full text


Co-saliency detection aims at discovering the common and salient objects in multiple images. It explores not only intra-image but extra inter-image visual cues, and hence compensates the shortages in single-image saliency detection. The performance of co-saliency detection substantially relies on the explored visual cues. However, the optimal cues typically vary from region to region. To address this issue, we develop an approach that detects co-salient objects by region-wise saliency map fusion. Specifically, our approach takes intra-image appearance, inter-image correspondence, and spatial consistence into account, and accomplishes saliency detection with locally adaptive saliency map fusion via solving an energy optimization problem over a graph. It is evaluated on a benchmark dataset and compared to the state-of-the-art methods. Promising results demonstrate its effectiveness and superiority.




Tsai, C. C., Qian, X., & Lin, Y. Y. (2017). Image co-saliency detection via locally adaptive saliency map fusion. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 1897–1901). Institute of Electrical and Electronics Engineers Inc.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free