Robust Background Template for Saliency Detection

In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super-pixels in terms of the optimized background, where the reconstruction error represents a saliency measure. Based on the optimized template, two saliency maps are generated by dense and sparse representation. Finally, the Bayesian framework used to integrate the two saliency maps to obtain the final one. Experimental results show that the proposed method performs favorably against eight state-of-the-art methods. In addition, the proposed method is shown to be more effective in highlighting the challenging salient objects that touch the image boundary.