{"title":"蝴蝶分类与机器学习方法的Android应用程序","authors":"Lili Zhu, P. Spachos","doi":"10.1109/GlobalSIP45357.2019.8969441","DOIUrl":null,"url":null,"abstract":"In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies by training and testing on a butterfly dataset, and determined the optimal model for an Android application. This application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from gallery.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Butterfly Classification with Machine Learning Methodologies for an Android Application\",\"authors\":\"Lili Zhu, P. Spachos\",\"doi\":\"10.1109/GlobalSIP45357.2019.8969441\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies by training and testing on a butterfly dataset, and determined the optimal model for an Android application. This application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from gallery.\",\"PeriodicalId\":221378,\"journal\":{\"name\":\"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)\",\"volume\":\"144 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GlobalSIP45357.2019.8969441\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobalSIP45357.2019.8969441","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Butterfly Classification with Machine Learning Methodologies for an Android Application
In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies by training and testing on a butterfly dataset, and determined the optimal model for an Android application. This application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from gallery.
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