{"title":"基于学习属性的人脸图像检索基准","authors":"Tomohiro Hibino, Haoran Xie, K. Miyata","doi":"10.1109/NicoInt50878.2020.00035","DOIUrl":null,"url":null,"abstract":"Choosing the most desirable image from many images can be a hard task for a user. In this study, we developed an image selection system based on 4 ways of calculating similarity. We conducted an experiment to validate the usability of the system and decide the best method for recommendation. It was confirmed that our system significantly helps users and Principal Component Analysis is a good way to calculate characteristics from many images.","PeriodicalId":230190,"journal":{"name":"2020 Nicograph International (NicoInt)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Benchmark on Face Image Retrieval From Learning Attributes\",\"authors\":\"Tomohiro Hibino, Haoran Xie, K. Miyata\",\"doi\":\"10.1109/NicoInt50878.2020.00035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Choosing the most desirable image from many images can be a hard task for a user. In this study, we developed an image selection system based on 4 ways of calculating similarity. We conducted an experiment to validate the usability of the system and decide the best method for recommendation. It was confirmed that our system significantly helps users and Principal Component Analysis is a good way to calculate characteristics from many images.\",\"PeriodicalId\":230190,\"journal\":{\"name\":\"2020 Nicograph International (NicoInt)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Nicograph International (NicoInt)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NicoInt50878.2020.00035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Nicograph International (NicoInt)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NicoInt50878.2020.00035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Benchmark on Face Image Retrieval From Learning Attributes
Choosing the most desirable image from many images can be a hard task for a user. In this study, we developed an image selection system based on 4 ways of calculating similarity. We conducted an experiment to validate the usability of the system and decide the best method for recommendation. It was confirmed that our system significantly helps users and Principal Component Analysis is a good way to calculate characteristics from many images.
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