{"title":"DeepFoodCam:基于dcnn的实时移动食品识别系统","authors":"Ryosuke Tanno, Koichi Okamoto, Keiji Yanai","doi":"10.1145/2986035.2986044","DOIUrl":null,"url":null,"abstract":"Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":"{\"title\":\"DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System\",\"authors\":\"Ryosuke Tanno, Koichi Okamoto, Keiji Yanai\",\"doi\":\"10.1145/2986035.2986044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.\",\"PeriodicalId\":91925,\"journal\":{\"name\":\"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"45\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2986035.2986044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2986035.2986044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System
Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.
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