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In this paper, we propose an improved mechanism for saliency detection. Firstly, based on a neoteric background prior selecting four corners of an image as background, color and spatial contrast with each super-pixel are being used to obtain a coarse map. Then, we put the Objectness labels as foreground prior based on part of information of the former map to construct another map. Further, an original energy function is applied to optimize both of them respectively and single-layer saliency map is formed by merging the above two maps. Finally, to settle the scale problem, we obtain our multi-layer saliency map by presenting an integration algorithm to take advantage of multiple saliency maps. Quantitative and qualitative experiments on three datasets demonstrate that our method performs favorably against the state-of-the-art algorithm.

Saliency Detection Combining Multi-layer Integration Algorithm with Background Prior and Energy Function