Paper Abstract and Keywords |
Presentation |
2023-02-21 10:45
Classification of illustration images by mood using a combination of supervised and unsupervised learning Keisuke Kubota, Masahiro Okuda (Doshisha Univ.) ITS2022-45 IE2022-62 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
With the spread of animation, games, and animated movies, users are increasingly drawing and posting illustrations on social networking services (SNS), and users are viewing illustrations more frequently. The ambiguous "atmosphere" of illustrations is thought to influence user preferences, and if illustrations could be classified according to atmosphere, they would be useful for recommendation and search. However, it is difficult to assign clear class labels to ambiguous "moods," and it is difficult to apply conventional label-based classification as is. In addition, there are many cases in which low-level features such as colors and edges of an image are similar, but the atmosphere of the image is different.
Therefore, in this study, pseudo-labels that indirectly contribute to the classification of the atmosphere are assigned, and feature vectors are obtained by learning with a CNN. We then propose a method for clustering images based on the feature vectors using the K-means method. Experimental results show that the proposed method can achieve more human-like classification than conventional methods on a human-classified dataset. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
machine learning / multilabel classification / image classification / clustering / dataset / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 385, IE2022-62, pp. 19-24, Feb. 2023. |
Paper # |
IE2022-62 |
Date of Issue |
2023-02-14 (ITS, IE) |
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) |
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ITS2022-45 IE2022-62 |
Conference Information |
Committee |
IE ITS ITE-MMS ITE-ME ITE-AIT |
Conference Date |
2023-02-21 - 2023-02-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Hokkaido Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image Processing, etc. |
Paper Information |
Registration To |
IE |
Conference Code |
2023-02-IE-ITS-MMS-ME-AIT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Classification of illustration images by mood using a combination of supervised and unsupervised learning |
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machine learning |
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multilabel classification |
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image classification |
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clustering |
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dataset |
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1st Author's Name |
Keisuke Kubota |
1st Author's Affiliation |
Doshisha University (Doshisha Univ.) |
2nd Author's Name |
Masahiro Okuda |
2nd Author's Affiliation |
Doshisha University (Doshisha Univ.) |
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Speaker |
Author-1 |
Date Time |
2023-02-21 10:45:00 |
Presentation Time |
15 minutes |
Registration for |
IE |
Paper # |
ITS2022-45, IE2022-62 |
Volume (vol) |
vol.122 |
Number (no) |
no.384(ITS), no.385(IE) |
Page |
pp.19-24 |
#Pages |
6 |
Date of Issue |
2023-02-14 (ITS, IE) |
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