计算机工程与应用2019，Vol.55Issue(20)：134-138,5.DOI:10.3778/j.issn.1002-8331.1806-0330
基于多种LBP特征集成学习的车标识别
Vehicle-Logo Recognition Based on Ensemble Learning with Multiple LBP Features
摘要
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.关键词
车标定位/CSLBP算子/支持向量机(SVM)/集成学习Key words
vehicle-logo position/Center Symmetric Local Binary Pattern(CSLBP)/Support Vector Machine(SVM)/ensemble learning分类
信息技术与安全科学引用本文复制引用
李哲,于梦茹..基于多种LBP特征集成学习的车标识别[J].计算机工程与应用,2019,55(20):134-138,5.基金项目
陕西省科技统筹创新工程项目(No.2016KTZDGY02-04-02) （No.2016KTZDGY02-04-02）
陕西省重点研发计划(No.2017GY-060). （No.2017GY-060）
