Salient region detection through sparse reconstruction and graph-based ranking

• We engaged sparse representation and graph-base ranking to calculate salient regions.
• We compute the sparse representation measure and uniqueness of the features.
• Graph-based scheme is utilized to rank the background and foreground seeds.
• Gaussian and Bayes procedures are used to produce smooth and precise saliency map.
• Moreover, we detect the outline of object of interest to generate precise saliency cut.

In this paper, we propose a salient region detection algorithm from the point of view of unique and compact representation of individual image. In first step, the original image is segmented into super-pixels. In second step, the sparse representation measure and uniqueness of the features are computed. Then both are ranked on the basis of the background and foreground seeds respectively. Thirdly, a location prior map is used to enhance the foci of attention. We apply the Bayes procedure to integrate computed results to produce smooth and precise saliency map. We compare our proposed algorithm against the state-of-the-art saliency detection methods using four of the largest widely available standard data-bases, experimental results specify that the proposed algorithm outperforms. We also show that how the saliency map of the proposed method is used to discover outline of object, furthermore using this outline our method produce the saliency cut of the desired object.