RGB-D图像的贝叶斯显著性检测（英文）

发布时间：2018-07-15 18:47

【摘要】：In this paper,we propose a saliency detection model for RGB-D images based on the contrasting features of color and depth within a Bayesian framework.The depth feature map is extracted based on superpixel contrast computation with spatial priors.We model the depth saliency map by approximating the density of depth-based contrast features using a Gaussian distribution.Similar to the depth saliency computation,the color saliency map is computed using a Gaussian distribution based on multi-scale contrasts in superpixels by exploiting low-level cues.By assuming that color-and depth-based contrast features are conditionally independent,given the classes,a discriminative mixed-membership naive Bayes(DMNB)model is used to calculate the final saliency map from the depth saliency and color saliency probabilities by applying Bayes’theorem.The Gaussian distribution parameter can be estimated in the DMNB model by using a variational inference-based expectation maximization algorithm.The experimental results on a recent eye tracking database show that the proposed model performs better than other existing models.
[Abstract]:In this paper we propose a saliency detection model for RGB-D images based on the contrasting features of color and depth within a Bayesian framework.The depth feature map is extracted based on superpixel contrast computation with spatial priors.We model the depth saliency map by approximating the density of depth-based contrast features using a Gaussian distribution. Similar to the depth saliency computation / the color saliency map is computed using a Gaussian distribution based on Multi-scale contrasts in superpixels by exploiting low-level cues.by assuming that color-and depth-based contrast features are conditionally independent given the classesa discriminative mixed-membership naive Bayes (DMNB) model is used to calculate the final saliency map from the depth saliency and color saliency probabilities by applying Bayes) the Gaussian distribution parameter can be estimated in the DMNB model by using a variational inference-based expectation maximization algorithm. The experimental results on a recent eye tracking database Show that the proposed model performs better than other existing models.
【作者单位】： Higher
【基金】：supported by the Innovation Group Plan of Beijing Academy of Science and Technology(IG201506N) the Youth Core Plan of Beijing Academy of Science and Technology(2015-16)
【分类号】：TP391.41

【相似文献】