A saliency detection model based on sparse features and visual acuity

In this paper, we propose a novel computational model of visual attention based on the relevant characteristics of the Human Visual System (HVS). The input image is firstly divided into small image patches. Then the sparse features for each image patch are extracted based on the learned sparse coding basis. The human visual acuity is adopted in the calculation of the center-surround feature differences for saliency detection. In addition, the neighboring image patches for computing the saliency value of each center image patch are selected based on the characteristics of HVS. Experimental results show that the proposed saliency detection algorithm outperforms other existing schemes tested with a large public image database.