{"title":"汽车品牌识别中几种特征提取方法的比较分析","authors":"Shengmei Lin, Chihang Zhao, Xingzhi Qi","doi":"10.1109/ICSENST.2016.7796337","DOIUrl":null,"url":null,"abstract":"Several feature extraction methods, such as the local energy shape histogram, the local binary pattern model and the gradient histogram, are comparatively used to characterize vehicle face images, and Support Vector Machines (SVM) are proposed to classify vehicle brands. Theoretical analysis and experimental results show that the vehicle brand recognition method based on HOG feature extraction and SVM exceeds the other four methods, and the recognition rate is up to 92.40%.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Comparative analysis of several feature extraction methods in vehicle brand recognition\",\"authors\":\"Shengmei Lin, Chihang Zhao, Xingzhi Qi\",\"doi\":\"10.1109/ICSENST.2016.7796337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Several feature extraction methods, such as the local energy shape histogram, the local binary pattern model and the gradient histogram, are comparatively used to characterize vehicle face images, and Support Vector Machines (SVM) are proposed to classify vehicle brands. Theoretical analysis and experimental results show that the vehicle brand recognition method based on HOG feature extraction and SVM exceeds the other four methods, and the recognition rate is up to 92.40%.\",\"PeriodicalId\":297617,\"journal\":{\"name\":\"2016 10th International Conference on Sensing Technology (ICST)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 10th International Conference on Sensing Technology (ICST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSENST.2016.7796337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 10th International Conference on Sensing Technology (ICST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENST.2016.7796337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparative analysis of several feature extraction methods in vehicle brand recognition
Several feature extraction methods, such as the local energy shape histogram, the local binary pattern model and the gradient histogram, are comparatively used to characterize vehicle face images, and Support Vector Machines (SVM) are proposed to classify vehicle brands. Theoretical analysis and experimental results show that the vehicle brand recognition method based on HOG feature extraction and SVM exceeds the other four methods, and the recognition rate is up to 92.40%.