计算机与数字工程2025，Vol.53Issue(3)：803-810,8.DOI:10.3969/j.issn.1672-9722.2025.03.032
一种基于Latent SVM的车辆图像分类方法
A Vehicle Image Classification Method Based on Latent SVM
摘要
Abstract
In order to solve the problem of traffic congestion caused by a large number of heavy vehicles in the city and the loss of information in the process of feature extraction of traditional image classification,which leads to the decrease of classification accuracy,this paper proposes a vehicle image classification method based on Latent SVM.With more detailed vehicle image classi-fication,traffic controllers can quickly locate heavy vehicles and drive them away from the city center,thereby greatly easing traf-fic.By adopting a new part localization algorithm,the method automatically finds a set of distinct parts in each class of vehicle,and uses the features of these parts and the spatial relationship between them to train the model of each class.In addition,a multi-class data mining method is used to filter difficult negative samples in the training process.Finally,these trained individual models are combined to classify vehicle brands and models with high accuracy.Experimental results on CompCars datasets show that the pro-posed method has satisfactory feature extraction ability and more accurate classification ability.关键词
图像分类/零件定位/Latent SVM/特征提取Key words
image classification/parts localisation/Latent SVM/feature extraction分类
信息技术与安全科学引用本文复制引用
杜小龙,黄树成..一种基于Latent SVM的车辆图像分类方法[J].计算机与数字工程,2025,53(3):803-810,8.基金项目
国家自然科学基金项目"基于鲁棒表现建模的目标跟踪方法研究"(编号:61772244)资助. （编号:61772244）
