Study of visual saliency detection via nonlocal anisotropic diffusion equation

• We propose a novel method for the visual saliency detection problem.
• The proposed method is based on nonlocal anisotropic diffusion equation.
• An iteration of diffusion is used to formulate the visual attention spreading.
• The boundary prior is exploited to diffusion from boundary to the entire image.
• The proposed method has a promising performance in terms of accuracy and speed.

In this paper, a nonlocal anisotropic diffusion equation is constructed to model the evolution of visual saliency. A new multi-directions discretization scheme is adopted to solve the equation. Visual attention diffusion is modeled as a series of diffusion progresses until achieving a stable status. So two stages of diffusion are involved. In the first stage, image boundaries are used to guide the background iterative diffusion process, and the initial saliency seeds are obtained by sorting. In the second stage, an optimization of saliency seeds is executed, and the good saliency seeds are strengthened and updated during each iteration. The final saliency map is obtained through iteratively diffusing saliency scores from the optimal saliency seeds (i.e. the most representative salient elements). Extensive experimental results on two large benchmark databases demonstrate the effectiveness of the proposed method.