复杂背景下，传统显著性检测方法经常遭遇检测结果不稳定和准确率低的问题。针对这些问题，提出一种基于贝叶斯框架融合深度信息的显著性检测方法。首先利用全局对比、局部对比和前景背景对比方法获取颜色显著图，并利用非均质中心-邻居差异的深度对比方法获取深度显著图。其次采用贝叶斯模型融合颜色显著图和深度显著图，获得输出显著图。实验结果表明，本文的方法能有效检测出复杂背景下的显著目标，并在公开的NLPR-RGBD数据集和NJU-DS400 数据集上取得较高检测精确度。

In the complex background, the traditional saliency detection methods often encounter the problems of unstable detection results and low accuracy. To address this problem, a saliency detection method fused depth information based on Bayesian framework is proposed. Firstly, the color saliency map is obtained by using a variety of contrast methods which includes global contrast, local contrast and foreground-background contrast, and the depth saliency map is obtained by using the depth contrast method based on the anisotropic center-surround difference. Secondly, using the Bayesian model to fuse the color-based saliency map and the depth-based saliency map. The experimental results show that the proposed method can effectively detect the salient targets under complex background and achieve higher detection accuracy on the published NLPR-RGBD dataset and NJU-DS400 dataset.