IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2020-12-23 14:00
Analysis of human subjective evaluation using deep neural networks
Yoshiyuki Sato (Tohoku Univ.), Kazuya Matsubara, Yuji Wada (Ritsmeikan Univ.), Satoshi Shioiri (Tohoku Univ.) HIP2020-68
Abstract (in Japanese) (See Japanese page) 
(in English) In this research, we constructed an deep learning model to learn and predict several different subjective judgments by human (desire to eat, whether it is made for young people, etc.) for food images. We show that our deep learning model successfully predict the different human subjective judgements. Furthermore, we analyze the parts of images which contribute to the judgment of the deep learning model using a visual explanation technique. We show that the model uses relative narrow regions of the images when it judges higher rating for higher-rated images by human raters. On the other hand, the model uses relatively broad regions when it judges lower rating for lower-rated images by humans raters. Our future work is to compare the visual explanation of the model to the factor that affects the subjective ratings by humans.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning model / Subjective rating prediction / Food image / Visual explanation / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 306, HIP2020-68, pp. 77-80, Dec. 2020.
Paper # HIP2020-68 
Date of Issue 2020-12-15 (HIP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 HIP2020-68

Conference Information
Committee HIP  
Conference Date 2020-12-22 - 2020-12-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To HIP 
Conference Code 2020-12-HIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of human subjective evaluation using deep neural networks 
Sub Title (in English)  
Keyword(1) Deep learning model  
Keyword(2) Subjective rating prediction  
Keyword(3) Food image  
Keyword(4) Visual explanation  
1st Author's Name Yoshiyuki Sato  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Kazuya Matsubara  
2nd Author's Affiliation Ritsmeikan University (Ritsmeikan Univ.)
3rd Author's Name Yuji Wada  
3rd Author's Affiliation Ritsmeikan University (Ritsmeikan Univ.)
4th Author's Name Satoshi Shioiri  
4th Author's Affiliation Tohoku University (Tohoku Univ.)
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2020-12-23 14:00:00 
Presentation Time 30 minutes 
Registration for HIP 
Paper # HIP2020-68 
Volume (vol) vol.120 
Number (no) no.306 
Page pp.77-80 
Date of Issue 2020-12-15 (HIP) 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan