关键词: 多尺度, 贝叶斯模型, 背景先验, 显著目标

Abstract: In order to overcome the problems of nonuniform in salient object highlight and weak ability of anti-background existing in traditional approaches of saliency detection, a salient object detection method based on multi-scale contrast and Bayesian model is proposed. Firstly, the source image is segmented into a series of superpixels with same color feature, and multi-scale segmentation maps are calculated by K-means algorithm. Secondly, background priors and convex-hull center prior computing multi-scale saliency maps are adopted, and then a coarse saliency map is calculated through weighted summation. Finally, based on the rough region, a prior map is computed for the Bayesian model to achieve the final saliency map. Compared with 6 state-of the-art methods on publicly available datasets（MSRA-1000）, the simulation results demonstrate that the salient object detection approach proposed in this paper performs more uniform in highlight salient object,  with higher precision ratio and lower mean absolute error.

Key words: multi-scale, Bayesian model, background priors, salient object