Sparse Embedding Visual Attention Systems Combined with Edge Information

The general computational models of visual attention are to obtain multi-scale feature maps in terms of visual properties like intensity, color and orientation, and then combine them to get one saliency map. But due to the lack of object edge information and reasonable feature combination strategy, the visual saliency map of the image is a blur map. Being aware of these, we propose a new scheme for saliency extraction. In this paper, we firstly put forward a sparse embedding feature combination strategy, inspired by sparse representation. The strategy is used to combine the salient regions from the individual feature maps based on a novel feature sparse indicator that measures the contribution of each map to saliency. Then we combine traditional visual attention with edge information. Results on different scene images show that our method outperforms other traditional feature combination strategies.