| Computer Technology, Information Engineering |
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| Nonparametric RGB-D scene parsing based on Markov random field model |
| FEI Ting ting,GONG Xiao jin |
| Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract An effective nonparametric method was proposed for RGB-D scene parsing. The method is based upon the label transferring scheme, which includes label pool construction, bi-directional superpixel matching -nd label transferring stages. Compared to traditional parametric RGB-D scene parsing methods, the approach requires no tedious training stage, which makes it simple and efficient. In contrast to previous nonparametric techniques, our method not only incorporate geometric contexts at all the stages, but also propose a bi-directional scheme for superpixel matching in order to reduce mismatching. Then a collaborative representation based classification (CRC) mechanism was built for Markov random field (MRF), and parsing result was achieved through minimizing the energy function via Graph Cuts. The effectiveness of the approach was validated both on the indoor NYU Depth V1 dataset and the outdoor KITTI dataset. The approach outperformed both state-of-the-art RGB-D parsing techniques and a classical nonparametric superparsing method. The algorithm can be applied to different scenarios, having a strong practical value．
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Published: 23 July 2016
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基于马尔科夫随机场的非参数化RGB-D场景理解
针对RGB-D场景下的场景理解问题,提出高效的基于标签传递机制的非参数化场景理解算法.该算法主要分为标签源构建、超像素双向匹配和标签传递三个步骤.与传统的参数化RGB-D场景理解方法相比,该算法不需要繁琐的训练,具有简单高效的特点.与传统的非参数化场景理解方法不同,该算法在系统的各个设计环节都有效利用了深度图提供的三维信息,在超像素匹配环节提出双向匹配机制,以减少特征误匹配；构建基于协同表示分类（CRC）的马尔科夫随机场（MRF）,用Graph Cuts方法求出最优解,获得场景图像每个像素的语义标签.该算法分别在室内的NYU-V1数据集和室外的KITTI数据集上进行实验.实验结果表明,与现有算法相比,该算法取得了显著的性能提升, 对室内、外场景均适用.
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