Sparse embedding visual attention system combined with edge information

Numerous computational models of visual attention have been suggested during the last two decades. But, there are still some challenges such as which of early visual features should be extracted and how to combine these different features into a unique “saliency” map. According to these challenges, we proposed a sparse embedding visual attention system combined with edge information, which is described as a hierarchical model in this paper. In the first stage, we extract edge information besides color, intensity and orientation as early visual features. In the second stage, we present a novel sparse embedding feature combination strategy. Results on different scene images show that our model outperforms other visual attention computational models.