Saliency detection integrating global and local information

In this paper, we propose a novel visual saliency detection algorithm. The saliency of image region is defined as its global and local information. Firstly, we construct background-based map based on a novel multi-feature similarity metric by adjusting the weight of different features varied with image content, then integrated with center prior and Objectless measure into global saliency map. Secondly, a robust locality-based coding method is used to extract image local saliency cues by introducing effective codebooks selection rule and codebook element's reliability into reconstruction. Finally, we propose a novel integration mechanism to incorporate global and local saliency map for performance improvement. In terms of experimental results analysis on four benchmark datasets, the superiority of proposed algorithm is adequately demonstrated.