{"title":"基于方向梯度直方图的支持向量机纹理图像分类","authors":"Hasan Demir","doi":"10.5152/IUJEEE.2018.1814","DOIUrl":null,"url":null,"abstract":"Herein, using support vector machines, texture images were classified based on the histogram of oriented gradients, from which feature vectors were obtained. In addition, the success rate was examined for the feature vectors with different dimensions and the minimum length of a feature vector for performing classification was determined to be 288 elements.","PeriodicalId":256344,"journal":{"name":"Istanbul University - Journal of Electrical and Electronics Engineering","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Classification of Texture Images Based on the Histogram of Oriented Gradients Using Support Vector Machines\",\"authors\":\"Hasan Demir\",\"doi\":\"10.5152/IUJEEE.2018.1814\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Herein, using support vector machines, texture images were classified based on the histogram of oriented gradients, from which feature vectors were obtained. In addition, the success rate was examined for the feature vectors with different dimensions and the minimum length of a feature vector for performing classification was determined to be 288 elements.\",\"PeriodicalId\":256344,\"journal\":{\"name\":\"Istanbul University - Journal of Electrical and Electronics Engineering\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Istanbul University - Journal of Electrical and Electronics Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5152/IUJEEE.2018.1814\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Istanbul University - Journal of Electrical and Electronics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5152/IUJEEE.2018.1814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of Texture Images Based on the Histogram of Oriented Gradients Using Support Vector Machines
Herein, using support vector machines, texture images were classified based on the histogram of oriented gradients, from which feature vectors were obtained. In addition, the success rate was examined for the feature vectors with different dimensions and the minimum length of a feature vector for performing classification was determined to be 288 elements.
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