基于多特征融合和条件随机场的道路分割

针对复杂交通场景图像中路面分割难度大和分割边缘粗糙的问题，提出了一种基于多特征融合和条件随机场的道路分割方法.首先，提取图像的纹理基元特征与颜色特征；然后，将道路分割问题视为一个基于像素的二分类问题，融合所提取的两种特征，使用SVM分类器实现对交通场景图像中路面区域与背景区域的粗糙划分；最后，利用全连接条件随机场中的颜色与位置约束，对分割结果进行优化，获得更加平滑的分割边缘，并与其他分割算法进行对比.实验结果表明，基于多特征融合与条件随机场的道路分割算法获得了95.37%的平均分割准确率和94.55%的平均像素精度.

Road Segmentation Method Based on Multi-Feature Fusion and Conditional Random Field

In the complex traffic scene image, road segmentation is difficult and the edges of the segmentation are rough. In order to solve this problem, a road segmentation method based on multi-feature fusion and conditional random field is proposed. Firstly, the textons and color features of the image are extracted from the traffic image. Then, the road segmentation problem is regarded as a pixel-based binary classification problem. The extracted texton features and color features are fused and input into the SVM classifier, which can achieve the coarse segmentation of the road area and the background area in the traffic image. Finally, by using the color and position constraints of the fully connected conditional random field to optimize segmentation results, a smoother segmentation edge can be obtained and compared with other segmentation algorithms. The experimental results demonstrate that road segmentation method that based on the multi-feature fusion and the conditional random field achieves 95.37% of average segmentation accuracy and 94.55% of mean pixel accuracy.