基于协方差描述子和LogitBoost的交通场景图像分割

Segmentation of Traffic Scene Based on Covariance Descriptor and LogitBoost Classifier

针对传统方法直接采用图像特征参与图像分割时存在的特征冗余且分割准确率低的问题,提出了一种基于协方差描述子和LogitBoost的交通场景图像分割方法.采用运动结构特征、纹理和HOG特征描述交通场景,并利用协方差描述子进行特征融合以消除特征冗余;采用多类LogitBoost分类器进行图像分割,提高了交通场景分割的精度.在公共测试视频数据库CamVid中测试和评估了所提出的算法,结果表明了该方法的有效性.

In order to overcome the drawback of traditional method directly using image features to classify images,which will produce the problems of feature redundancy and low accuracy,a new approach based on Covariance Descriptor and LogitBoost was proposed for the image segmentation of traffic scene.The motion and structure,texture and HOG features were extracted for segmenting image.Meanwhile,the covariance descriptor was used to fuse the features mentioned above to reduce the feature redundancy.The multiclass LogitBoost classifier was used for image segmentation to improve the accuracy of segmentation.Experiments on the public CamVid dataset were preformed to test and evaluate the proposed method,and the results showed that this method was effective.