关键词: 边界信息, 超像素, 加权中值滤波, 贝叶斯法则

Abstract: The traditional salient region detection method using the color feature of the image and the super-pixels pretreatment has not been able to solve the problem of insufficient smearing effect and mistaken detection. According to the problem, this paper proposes a method for the salient region detection based on boundary information and color characteristics. In order to obtain better image boundary information and remove the noise, the source image is processed by multiple Weighted Median Filtering（WMF） and segmented by a Simple Linear Iterative Clustering（SLIC） to find the natural boundary in the image through the color, brightness and other information. Combining boundary information and the color feature of the super-pixels obtained by the SLIC segmentation, the probability of the super-pixels with the SLIC segmentation locating in the saliency map obtained by Graph-based segmentation is conditional likelihood. Then the final saliency map is gotten in the Bayesian framework. The experimental results which are applied to a public benchmark datasets（MSRA-1000） results show that the proposed algorithm has a better effect on the precision and recall than the seven classical algorithms, the algorithm achieves the highest precision value of 98.03%.

Key words: boundary information, super-pixels, weighted median filtering, Bayesian framework