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Paper Abstract and Keywords
Presentation 2021-09-17 14:00
A CNN model Using Neural Style Features for Predicting Aesthetic Impressions Score Distribution
Yuya Ohagi (Kwansei Gakuin Univ.), Kensuke Tobitani (Univ. of Nagasaki), Iori Tani (Kobe Univ.), Sho Hashimoto (Seinan Gakuin Univ.), Noriko Nagata (Kwansei Gakuin Univ.) MVE2021-14
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we propose a method for predicting the probability distribution of aesthetic impression scores considering individual differences in impression evaluations using a deep neural network. We adopted neural style features, which potentially have relationships with visual impressions as explanatory variables. Then, we constructed a convolutional neural network (CNN) that estimated the probability distribution of impression scores based on product images. Next, we visualized attention maps that represented image areas that contribute to impression scores by using Grad-CAM. We also conducted an impression evaluation experiment to relate individual impression scores to the image areas that each participant considered important. Finally, we confirmed the similarity among the image areas by comparing the attention maps and the experimental results.
Keyword (in Japanese) (See Japanese page) 
(in English) kansei (affective) engineering / visual impression / impression estimation / visualization / deep learning / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 179, MVE2021-14, pp. 33-37, Sept. 2021.
Paper # MVE2021-14 
Date of Issue 2021-09-10 (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 MVE2021-14

Conference Information
Committee MVE  
Conference Date 2021-09-17 - 2021-09-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MVE 
Conference Code 2021-09-MVE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A CNN model Using Neural Style Features for Predicting Aesthetic Impressions Score Distribution 
Sub Title (in English)  
Keyword(1) kansei (affective) engineering  
Keyword(2) visual impression  
Keyword(3) impression estimation  
Keyword(4) visualization  
Keyword(5) deep learning  
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Keyword(7)  
Keyword(8)  
1st Author's Name Yuya Ohagi  
1st Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
2nd Author's Name Kensuke Tobitani  
2nd Author's Affiliation University of Nagasaki (Univ. of Nagasaki)
3rd Author's Name Iori Tani  
3rd Author's Affiliation Kobe University (Kobe Univ.)
4th Author's Name Sho Hashimoto  
4th Author's Affiliation Seinan Gakuin University (Seinan Gakuin Univ.)
5th Author's Name Noriko Nagata  
5th Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
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Speaker Author-1 
Date Time 2021-09-17 14:00:00 
Presentation Time 30 minutes 
Registration for MVE 
Paper # MVE2021-14 
Volume (vol) vol.121 
Number (no) no.179 
Page pp.33-37 
#Pages 5 
Date of Issue 2021-09-10 (MVE) 

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