{"title":"基于颜色的haar类特征的多类增强","authors":"Wen-Chung Chang, Chih-Wei Cho","doi":"10.1109/SITIS.2007.119","DOIUrl":null,"url":null,"abstract":"This paper presents a multi-class boosting algorithm employing color-based Haar-like features. Traditional multi-class boosting algorithms basically regard multi-class problems as extensions of two-class problems. In particular, additional strong classifiers must be parallelly extended once the number of target classes increases. The idea in the proposed approach is to develop a single strong classifier which is capable of resolving multi-class problems. To make the multi-class algorithm tractable, the proposed system is required to select a set of weak classifiers which could classify multiple types of targets correctly. In contrast to standard Haar-like features that compute feature values based on gray level images, the seemingly novel Haar-like features require computation based on color images. Since the mapping from color image space to gray level image space is an epimorphism, detection algorithms using standard Haar-like features inevitably disregard color information available in original color images. Strong classifiers adopting the proposed color-based Haar-like features typically appear to have comparable performance, in the aspects of detection and correct classification rates, with fewer weak classifiers when compared with the one employing standard Haar-like features. The proposed boosting algorithm can improve system efficiency and resolve multi-class problems by a single strong classifier, whereas existing approaches are more complicated and the number of two-class classifiers could be relatively large. Our approach has been successfully validated in real traffic environments by performing experiments with a CCD camera mounted onboard a highway vehicle, where the targets are defined as passenger cars and motorcycles.","PeriodicalId":234433,"journal":{"name":"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Multi-class Boosting with Color-Based Haar-Like Features\",\"authors\":\"Wen-Chung Chang, Chih-Wei Cho\",\"doi\":\"10.1109/SITIS.2007.119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a multi-class boosting algorithm employing color-based Haar-like features. Traditional multi-class boosting algorithms basically regard multi-class problems as extensions of two-class problems. In particular, additional strong classifiers must be parallelly extended once the number of target classes increases. The idea in the proposed approach is to develop a single strong classifier which is capable of resolving multi-class problems. To make the multi-class algorithm tractable, the proposed system is required to select a set of weak classifiers which could classify multiple types of targets correctly. In contrast to standard Haar-like features that compute feature values based on gray level images, the seemingly novel Haar-like features require computation based on color images. Since the mapping from color image space to gray level image space is an epimorphism, detection algorithms using standard Haar-like features inevitably disregard color information available in original color images. Strong classifiers adopting the proposed color-based Haar-like features typically appear to have comparable performance, in the aspects of detection and correct classification rates, with fewer weak classifiers when compared with the one employing standard Haar-like features. The proposed boosting algorithm can improve system efficiency and resolve multi-class problems by a single strong classifier, whereas existing approaches are more complicated and the number of two-class classifiers could be relatively large. Our approach has been successfully validated in real traffic environments by performing experiments with a CCD camera mounted onboard a highway vehicle, where the targets are defined as passenger cars and motorcycles.\",\"PeriodicalId\":234433,\"journal\":{\"name\":\"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITIS.2007.119\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2007.119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-class Boosting with Color-Based Haar-Like Features
This paper presents a multi-class boosting algorithm employing color-based Haar-like features. Traditional multi-class boosting algorithms basically regard multi-class problems as extensions of two-class problems. In particular, additional strong classifiers must be parallelly extended once the number of target classes increases. The idea in the proposed approach is to develop a single strong classifier which is capable of resolving multi-class problems. To make the multi-class algorithm tractable, the proposed system is required to select a set of weak classifiers which could classify multiple types of targets correctly. In contrast to standard Haar-like features that compute feature values based on gray level images, the seemingly novel Haar-like features require computation based on color images. Since the mapping from color image space to gray level image space is an epimorphism, detection algorithms using standard Haar-like features inevitably disregard color information available in original color images. Strong classifiers adopting the proposed color-based Haar-like features typically appear to have comparable performance, in the aspects of detection and correct classification rates, with fewer weak classifiers when compared with the one employing standard Haar-like features. The proposed boosting algorithm can improve system efficiency and resolve multi-class problems by a single strong classifier, whereas existing approaches are more complicated and the number of two-class classifiers could be relatively large. Our approach has been successfully validated in real traffic environments by performing experiments with a CCD camera mounted onboard a highway vehicle, where the targets are defined as passenger cars and motorcycles.