关键词: 显著性检测, 多尺度, 背景种子, 先验概率, 贝叶斯模型

Abstract:

In this paper, a multi-scale Bayesian based saliency detection algorithm is proposed to improve the unsatisfactory accuracy of traditional Bayesian based saliency detection methods. Firstly, multi-scale superpixels are generated by segmenting the input image with superpixel segmentation algorithm（SLIC）. The background seeds are obtained according to the boundary information of superpixels, followed by the background prior evaluation with distance computation and multi-scale fusion. Secondly, the Harris operator is used to detect the corner points of the enhanced image to obtain the convex hulls. Multi-scale superpixels are fused and result in a convex hull prior. Then, the final prior is generated by combining the background prior and convex hull prior. Meanwhile, the observation likelihood probability is computed by using the color histogram. Finally, the saliency map is evaluated with Bayesian model according to the obtained prior probability map and observation likelihood probability. The experiments on public data sets of MSRA1000 and ECSSD show that the proposed algorithm achieves improved performance on both precision and recall compared to the other popular saliency detection methods.

Key words: saliency detection, multi-scale, background seeds, prior probability, Bayesian model