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Paper Abstract and Keywords
Presentation 2020-12-18 17:15
Rethinking the local similarity in content-based image retrieval
Longjiao Zhao (Nagoya Univ.), Yu Wang (Ritsumeikan Univ), Yoshiharu Ishikawa (Nagoya Univ.), Jien Kato (Ritsumeikan Univ) PRMU2020-68
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, Convolutional Neural Networks(CNN) have shown good performance in the image retrieval task. Especially, local convolutional features which are extracted by CNN have presented outstanding result. Therefore, most of the works study on the pooling method which embeds the local features to global features and evaluate the global similarity between two images with global features. However, the global similarity is hard to present the effect of fine-grained information which is very important to the image retrieval task. Here, we propose a method that utilizes the local similarity to evaluate the images’ similarity. To do this, we generate a local similarity tensor(LST) and evaluate its effect from two aspects: spatial scale and local scale. Moreover, we propose a mask to the LST by analyzing the geometric features of images. Experiments demonstrate that LST can achieve higher accuracy than the baseline method.
Keyword (in Japanese) (See Japanese page) 
(in English) image retrieval / local similarity / deep learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-68, pp. 172-176, Dec. 2020.
Paper # PRMU2020-68 
Date of Issue 2020-12-10 (PRMU) 
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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Conference Information
Committee PRMU  
Conference Date 2020-12-17 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Transfer learning and few shot learning 
Paper Information
Registration To PRMU 
Conference Code 2020-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Rethinking the local similarity in content-based image retrieval 
Sub Title (in English)  
Keyword(1) image retrieval  
Keyword(2) local similarity  
Keyword(3) deep learning  
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1st Author's Name Longjiao Zhao  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Yu Wang  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ)
3rd Author's Name Yoshiharu Ishikawa  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Jien Kato  
4th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ)
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Speaker Author-1 
Date Time 2020-12-18 17:15:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2020-68 
Volume (vol) vol.120 
Number (no) no.300 
Page pp.172-176 
#Pages 5 
Date of Issue 2020-12-10 (PRMU) 

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