Visual saliency detection using information divergence

The technique of visual saliency detection supports video surveillance systems by reducing redundant information and highlighting the critical, visually important regions. It follows that information about the image might be of great importance in depicting the visual saliency. However, the majority of existing methods extract contrast-like features without considering the contribution of information content. Based on the hypothesis that information divergence leads to visual saliency, a two-stage framework for saliency detection, namely information divergence model (IDM), is introduced in this paper. The term “information divergence” is used to express the non-uniform distribution of the visual information in an image. The first stage is constructed to extract sparse features by employing independent component analysis (ICA) and difference of Gaussians (DoG) filter. The second stage improves the Bayesian surprise model to compute information divergence across an image. A visual saliency map is finally obtained from the information divergence. Experiments are conducted on nature image databases, psychological patterns and video surveillance sequences. The results show the effectiveness of the proposed method by comparing it with 13 state-of-the-art visual saliency detection methods.