Salient Object Detection Via Harris Corner

In this paper, an fast and effective image saliency detection based on Harris Comer method is proposed. Different from most previous methods that mainly concentrate on boundary prior, we take both background and foreground information into consideration. First, a novel method is proposed to approximately locate the foreground object by using the convex hull from Harris corner. Then, the original image is segmented into super-pixels regions and the saliency values of different regions are divided into two parts to generate the corresponding background and foreground cue maps which are combined into a unified map. Finally, the unified map and the convex hull center-biased algorithm are combined to be the saliency map, which is then optimized by Bayesian perspective and saliency diffusion to get the final result. Experiments on publicly available data sets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.