RGB-D图像的贝叶斯显著性检测

本文提出了一种基于贝叶斯框架融合颜色和深度对比特征的RGB-D图像显著性检测模型.基于空间先验的超像素对比计算得到深度特征，并通过高斯分布近似深度对比特征概率密度建模深度显著图.类似于深度显著性计算，采用高斯分布计算多尺度超像素低层对比特征得到颜色显著图.假设在给定显著类别下颜色和深度对比特征条件独立，依据贝叶斯定理，由深度显著概率和颜色显著概率得到RGB-D图像显著性后验概率，并采用判别混合分量朴素贝叶斯（DMNB）模型进行计算，其中DMNB模型中的高斯分布参数由变分最大期望算法进行估计.在RGB-D图像显著性检测公开数据集的实验结果表明提出的模型优于现有的方法.

Bayesian Saliency Detection for RGB-D Images

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.