关键词: 车标定位, CSLBP算子, 支持向量机（SVM）, 集成学习

Abstract: In view of the difficult problem of the classification of vehicle-logo images, this paper proposes a new vehicle-logo recognition method based on ensemble learning with multiple LBP features. Firstly, the location of vehicle logo is roughly positioned based on the relative position relationship between the license plate and the vehicle logo. Then, according to the background texture feature of the vehicle logo, edge detection is used by different operators to achieve background ablation, and uses the projection method to accurately determine the vehicle logo position. Finally, the vehicle logo image segmentation uses the Center Symmetric Local Binary Pattern（CSLBP） to extract the neighborhood features of each pixel, which forms the fine texture features. It uses the LBP histogram algorithm to extract the spatial structure features of vehicle logo region. SVM and BP are used to train two features separately, get the voting decision and the classification list, and obtain the output class by the weighted sum rule fused decision matrix, which constructs an optimal ensemble classifier. Experimental results show that the recognition rate of the proposed algorithm is better than that of a single feature and classifier.

Key words: vehicle-logo position, Center Symmetric Local Binary Pattern（CSLBP）, Support Vector Machine（SVM）, ensemble learning