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PROJECT TITLE :

Image Co-Saliency Detection Via Locally Adaptive Saliency Map Fusion - 2017

ABSTRACT:

Co-saliency detection aims at discovering the common and salient objects in multiple pictures. It explores not solely intra-image but additional inter-image visual cues, and hence compensates the shortages in single-image saliency detection. The performance of co-saliency detection substantially depends on the explored visual cues. However, the optimal cues sometimes vary from region to region. To address this issue, we have a tendency to 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 consideration, and accomplishes saliency detection with domestically adaptive saliency map fusion via solving an energy optimization downside 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.


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