Image Co-Saliency Detection via Locally Adaptive Saliency Map Fusion Conference Paper uri icon

abstract

  • © 2017 IEEE. 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.

author list (cited authors)

  • Tsai, C., Qian, X., & Lin, Y.

citation count

  • 6

publication date

  • March 2017

publisher