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 |
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) |
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IMQ2019-40 IE2019-122 MVE2019-61 |
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) |
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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) |
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Keyword(1) |
image retargeting |
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multi-operator |
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deep reinforcement learning |
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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 |
5 |
Date of Issue |
2020-02-27 (IMQ, IE, MVE) |
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