Saliency detection via a multi-layer graph based diffusion model

Saliency detection is an important problem in computer vision area. In this paper, we propose a new multi-layer graph based diffusion (MLD) model for image saliency detection by adopting random walk with restart(RWR) model. Firstly, we compute background and foreground priors/cues, respectively for the input image on different scales. Then, we adopt the proposed diffusion model to obtain more reasonable and accurate background and foreground measurements. Finally, we combine both background and foreground measurements together to obtain a more accurate saliency estimation. One important aspect of the proposed multi-layer diffusion model is that it can conduct diffusion of saliency cues across different layers simultaneously and cooperatively and thus can share and communicate the saliency cues across different image scales. Experimental evaluations on four benchmark datasets demonstrate the benefits and effectiveness of the proposed method.