{"title":"基于粗糙集和支持向量机的目标识别新方法","authors":"Zhi-jun Guo, Xin He, Zhonghui Wei, G. Liang","doi":"10.1109/IASP.2010.5476053","DOIUrl":null,"url":null,"abstract":"Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine, a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership, so that some samples can be chosen by class membership to be trained. After pre-treatment, an iterative algorithm based on Rough Set and Support Vector Machines (IRSSVM) is introduced to design a classifier for recognizing two types of targets. The experiment results show that IRSSVM needs less training time and the classifier is simpler and has more generalization and higher recognition rate.","PeriodicalId":223866,"journal":{"name":"2010 International Conference on Image Analysis and Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new method of target recognition based on Rough Set and Support Vector Machine\",\"authors\":\"Zhi-jun Guo, Xin He, Zhonghui Wei, G. Liang\",\"doi\":\"10.1109/IASP.2010.5476053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine, a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership, so that some samples can be chosen by class membership to be trained. After pre-treatment, an iterative algorithm based on Rough Set and Support Vector Machines (IRSSVM) is introduced to design a classifier for recognizing two types of targets. The experiment results show that IRSSVM needs less training time and the classifier is simpler and has more generalization and higher recognition rate.\",\"PeriodicalId\":223866,\"journal\":{\"name\":\"2010 International Conference on Image Analysis and Signal Processing\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Image Analysis and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IASP.2010.5476053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Image Analysis and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IASP.2010.5476053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new method of target recognition based on Rough Set and Support Vector Machine
Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine, a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership, so that some samples can be chosen by class membership to be trained. After pre-treatment, an iterative algorithm based on Rough Set and Support Vector Machines (IRSSVM) is introduced to design a classifier for recognizing two types of targets. The experiment results show that IRSSVM needs less training time and the classifier is simpler and has more generalization and higher recognition rate.