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Paper Abstract and Keywords
Presentation 2024-03-15 13:30
Study on Leftovers Prediction from Food Images
Yuita Arum Sari, Atsushi Nakazawa (Okayama University) IMQ2023-86 IE2023-141 MVE2023-115
Abstract (in Japanese) (See Japanese page) 
(in English) Leftover analysis is a valuable tool used by dietitians and nutritionists to assess a patient's calorie intake in healthcare service. The traditional method involves using digital scales to measure the leftover food immediately after consumption. However, this approach is time-consuming since it requires quick movement to change from one food to another for analysis. Furthermore, this method cannot be repeated for further evaluation. Another method, known as the Comstock level, is used to determine the scale of the leftovers. This method involves visual assessment, where observers compare the food before and after consumption and assign a scale. However, visual methods can be unreliable as they are subjective, particularly when observers are tired or have a heavy workload, which can reduce their concentration levels. Therefore, this paper proposes a new method for measuring leftover food using multitask learning with a classification and regression approach. The study achieved the best results using ResNet101, with an MAE of 0.6074±0.0769, an R2 score of 0.7442±0.0483, and a food accuracy of 82.5%±2.6352 respectively in subjective evaluation. Meanwhile, in objective evaluation, the result of MAE is 0.1223±0.0436, R2 score 0.7795±0.02491, and food accuracy 87.9411%±2.667, respectively. The proposed approach can be applied efficiently, and it can be concluded that it is an effective method.
Keyword (in Japanese) (See Japanese page) 
(in English) leftover food images / leftover prediction / computer vision / image classification / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 432, IE2023-141, pp. 390-395, March 2024.
Paper # IE2023-141 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IMQ2023-86 IE2023-141 MVE2023-115

Conference Information
Committee IE MVE CQ IMQ  
Conference Date 2024-03-13 - 2024-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Sangyo Shien Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） 
Paper Information
Registration To IE 
Conference Code 2024-03-IE-MVE-CQ-IMQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Leftovers Prediction from Food Images 
Sub Title (in English)  
Keyword(1) leftover food images  
Keyword(2) leftover prediction  
Keyword(3) computer vision  
Keyword(4) image classification  
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1st Author's Name Yuita Arum Sari  
1st Author's Affiliation Okayama University (Okayama University)
2nd Author's Name Atsushi Nakazawa  
2nd Author's Affiliation Okayama University (Okayama University)
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Speaker Author-1 
Date Time 2024-03-15 13:30:00 
Presentation Time 20 minutes 
Registration for IE 
Paper # IMQ2023-86, IE2023-141, MVE2023-115 
Volume (vol) vol.123 
Number (no) no.430(IMQ), no.432(IE), no.433(MVE) 
Page pp.390-395 
#Pages 6 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 

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