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Paper Abstract and Keywords
Presentation 2020-03-05 09:20
Self-Play Reinforcement Learning for Fast Image Retargeting
Nobukatsu Kajiura, Satoshi Kosugi, Xueting Wang, Toshihiko Yamasaki, Kiyoharu Aizawa (UTokyo) IMQ2019-40 IE2019-122 MVE2019-61
Abstract (in Japanese) (See Japanese page) 
(in English) We address image retargeting, which is a task of adjusting input images into arbitrary sizes. In a previous method, they combine multiple operators and generate multiple retargeted images to find the optimal combination, which minimizes the distance between the original and the retargeted images. In this paper, to find the optimal combination more quickly, we propose a method of predicting the optimal operator step by step using a reinforcement learning agent. The advantage of this method is that it is hardly affected even if the number of operators increases. Since the distance between the input image and the retargeted image varies greatly depending on the image, it is difficult to use the evaluation function of the conventional method as a reward. In order to solve this problem, we propose that a reward based on self-play can be insensitive to changes in the value of the evaluation function. We conduct experiments, which show that our method achieves multi-operator image retargeting that is faster by four orders of magnitude and has the same performance as the previous method.
Keyword (in Japanese) (See Japanese page) 
(in English) image retargeting / multi-operator / deep reinforcement learning / self-play / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 457, MVE2019-61, pp. 127-131, March 2020.
Paper # MVE2019-61 
Date of Issue 2020-02-27 (IMQ, IE, MVE) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee IE IMQ MVE CQ  
Conference Date 2020-03-05 - 2020-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MVE 
Conference Code 2020-03-IE-IMQ-MVE-CQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Self-Play Reinforcement Learning for Fast Image Retargeting 
Sub Title (in English)  
Keyword(1) image retargeting  
Keyword(2) multi-operator  
Keyword(3) deep reinforcement learning  
Keyword(4) self-play  
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1st Author's Name Nobukatsu Kajiura  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Satoshi Kosugi  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Xueting Wang  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Toshihiko Yamasaki  
4th Author's Affiliation The University of Tokyo (UTokyo)
5th Author's Name Kiyoharu Aizawa  
5th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2020-03-05 09:20:00 
Presentation Time 25 minutes 
Registration for MVE 
Paper # IMQ2019-40, IE2019-122, MVE2019-61 
Volume (vol) vol.119 
Number (no) no.454(IMQ), no.456(IE), no.457(MVE) 
Page pp.127-131 
#Pages
Date of Issue 2020-02-27 (IMQ, IE, MVE) 


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