TY - JOUR AU - Wang, Songtao AU - Zhou, Zhen AU - Jin, Wei AU - Qu, Hanbing PY - 2018 DA - 2018/01/10 TI - Visual saliency detection for RGB-D images under a Bayesian framework JO - IPSJ Transactions on Computer Vision and Applications SP - 1 VL - 10 IS - 1 AB - 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. SN - 1882-6695 UR - https://doi.org/10.1186/s41074-017-0037-0 DO - 10.1186/s41074-017-0037-0 ID - Wang2018 ER -