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Paper Abstract and Keywords
Presentation 2019-02-19 16:00
A Study on Vectorizing Diet Preferences based on Large Foodlogging Data and DNN model
Yuji Goda (UTokyo), Sosuke Amano (foo.log Inc.), Yoko Yamakata, Kiyoharu Aizawa (UTokyo)
Abstract (in Japanese) (See Japanese page) 
(in English) In usual dietary management, dieticians use the food logs to see one’s wellness. However, this method is not handy for ordinary people, and also it becomes the bottleneck of the statistical analysis of diet preference. In this paper, an autoencoder-based method is proposed to process raw food log to one feature which can be handled statistically. Also the appropriate method to evaluate foodlog-feature is discussed. The proposed feature exceeded the scores of dieticians’ in our evaluating method.
Keyword (in Japanese) (See Japanese page) 
(in English) food / diet preference / food records / deep learning / autoencoder / / /  
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Conference Information
Committee ITS IE ITE-MMS ITE-HI ITE-ME ITE-AIT  
Conference Date 2019-02-19 - 2019-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ITE-ME 
Conference Code 2019-02-ME-IE-ITS-MMS-HI-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Vectorizing Diet Preferences based on Large Foodlogging Data and DNN model 
Sub Title (in English)  
Keyword(1) food  
Keyword(2) diet preference  
Keyword(3) food records  
Keyword(4) deep learning  
Keyword(5) autoencoder  
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1st Author's Name Yuji Goda  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Sosuke Amano  
2nd Author's Affiliation foo.log Inc. (foo.log Inc.)
3rd Author's Name Yoko Yamakata  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Kiyoharu Aizawa  
4th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2019-02-19 16:00:00 
Presentation Time 15 minutes 
Registration for ITE-ME 
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Volume (vol) vol.118 
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