RGB-D图像的贝叶斯显著性检测

doi:10.16383/j.aas.2017.e160141

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摘要

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.

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	王松涛
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	曲寒冰
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关键词 ：多尺度超像素分割, 判别混合分量朴素贝叶斯模型, 显著性检测, 深度特征图, RGB-D图像

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 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.

Key words： Multi-scale superpixels segmentation discriminative mixed-membership naive Bayes(DMNB)model saliency detection depth feature map RGB-D images

收稿日期: 2016-11-05

基金资助:

This work was supported by the Innovation Group Plan of Beijing Academy of Science and Technology (IG201506N) and the Youth Core Plan of Beijing Academy of Science and Technology (2015-16).

通讯作者: Songtao Wang received the M.S.degree from Harbin University Of Science and Technology (HUST) in 2009,and is currently a Ph.D.candidate at the Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province,HUST.He is also a Research Assistant at the Beijing Institute of New Technology Applications (BIONTA) and the Key Laboratory of Pattern Recognition,Beijing Academy of Science and Technology (BJAST).His research interests include pattern recognition and computer vision,especially the visual saliency detection in surveillance scenarios.Corresponding author of this paper.E-mail:wangsongtao1983@163.com E-mail: wangsongtao1983@163.com

作者简介: Zhen Zhou received the M.S.and Ph.D.degrees from Harbin University of Science and Technology (HUST),Harbin,China,in 1991 and 2005,respectively.E-mail:zhzh49@126.com;Hanbing Qu received the M.S.and Ph.D.degrees from Harbin Institute of Technology (HIT) and the Institute of Automation,Chinese Academy of Sciences (CASIA),in 2003 and 2007,respectively.E-mail:quhanbing@gmail.com;Bin Li received the MSc and Ph.D.degrees in computer science from Harbin Institute of Technology (HIT),Harbin,China,in 2000 and 2006,respectively.E-mail:lbn hit@sina.com

引用本文:

王松涛, 周真, 曲寒冰, 李彬. RGB-D图像的贝叶斯显著性检测. 自动化学报, 2017, 43(10): 1810-1828.
Songtao Wang, Zhen Zhou, Hanbing Qu, Bin Li. Bayesian Saliency Detection for RGB-D Images. Acta Automatica Sinica, 2017, 43(10): 1810-1828.

链接本文:

http://www.aas.net.cn/CN/10.16383/j.aas.2017.e160141 或 http://www.aas.net.cn/CN/Y2017/V43/I10/1810

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