{"title":"基于视觉词袋和深度特征的散装食品分类","authors":"Abdelmgeid A. Ali, Usama Mohammed, Rehab Nour","doi":"10.21608/kjis.2021.198376","DOIUrl":null,"url":null,"abstract":"The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image","PeriodicalId":115907,"journal":{"name":"Kafrelsheikh Journal of Information Sciences","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features\",\"authors\":\"Abdelmgeid A. Ali, Usama Mohammed, Rehab Nour\",\"doi\":\"10.21608/kjis.2021.198376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image\",\"PeriodicalId\":115907,\"journal\":{\"name\":\"Kafrelsheikh Journal of Information Sciences\",\"volume\":\"102 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Kafrelsheikh Journal of Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21608/kjis.2021.198376\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kafrelsheikh Journal of Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21608/kjis.2021.198376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features
The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image
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