In this paper, we propose a saliency detection model for RGB-D images based on the deep features of RGB images and depth images within a Bayesian framework. By analysing 3D saliency in the case of RGB images and depth images, the class-conditional mutual information is computed for measuring the dependence of deep features extracted using a convolutional neural network; then, the posterior probability of the RGB-D saliency is formulated by applying Bayes’ theorem. By assuming that deep features are Gaussian distributions, a discriminative mixed-membership naive Bayes (DMNB) model is used to calculate the final saliency map. The Gaussian distribution parameters can be estimated in the DMNB model by using a variational inference-based expectation maximization algorithm. The experimental results on RGB-D images from the NLPR dataset and NJU-DS400 dataset show that the proposed model performs better than other existing models.

中文翻译：

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贝叶斯框架下RGB-D图像的视觉显着性检测

在本文中，我们基于RGB图像的深度特征和贝叶斯框架内的深度图像，提出了RGB-D图像显着性检测模型。通过在RGB图像和深度图像的情况下分析3D显着性，计算了类条件互信息，以测量使用卷积神经网络提取的深层特征的依赖性。然后，应用贝叶斯定理来确定RGB-D显着性的后验概率。通过假设深层特征是高斯分布，可以使用判别式混合成员朴素贝叶斯（DMNB）模型来计算最终显着图。可以使用基于变分推断的期望最大化算法在DMNB模型中估计高斯分布参数。