针对传统图模型分割算法提取的物体边缘不够精细、难以适应复杂道路场景布局的问题,提出了一种基于多层图模型推理的道路场景分割(HGI)算法.该算法先将图像过分割为同质的超像素块,再采用随机森林模型训练超像素块的多类别回归器和相邻超像素的一致性回归器;然后用2种回归值计算马尔科夫随机场(MRF)模型的能量项,通过推理得到初始分割;最后为了解决超像素块包含多类别带来的分类混淆,在初始分割基础上构建像素级的全连接条件随机场模型,进行优化得到精细的分割结果.实验结果表明,采用HGI算法对人工标注数据库和真实拍摄的场景图像处理能够得到精细的分割边缘,能够解决超像素推理中的类别混淆问题,与传统的MRF图模型分割方法相比,在总体精度和平均召回率2个指标上分别提高了2％和3％.%A novel segmentation algorithm for road scenes based on hierarchical graph-based inference (HGI) is proposed to solve the problem that object boundaries extracted by existing graph-based segmentation algorithms are not fine enough and it is hard to adapt to complex road scene layouts.The algorithm first over-segments an image into small homogeneous regions called superpixels,and then the random forest model is used to train a multi-class regressor and a consistency regressor of superpixels.Regression results are then used to calculate the energy terms in a Markov random field (MRF) energy function.An initial segmentation of the image is obtained by using the superpixel MRF inference.A pixel-level labeling based on fully connected conditional random fields is constructed to avoid the label confusion caused by the superpixels and to get a fine segmentation finally.Experimental results show that the proposed algorithm solves the label confusion in the superpixel inference and gets fine segmentation boundaries for both the images in manually labeled datasets and the real road scenes.A comparison with the traditional MRF graph-based inference methods shows that the HGI algorithm provides improvements of 2％ and 3％ on the overall precision and per-class average metrics,respectively.
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