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Paper Abstract and Keywords
Presentation 2013-01-23 11:40
Keypoint selection based on Diverse Density for image retrieval
Keita Yuasa, Toshikazu Wada, Hiroshi Oike, Jun Sakata (Wakayama Univ.) PRMU2012-91 MVE2012-56
Abstract (in Japanese) (See Japanese page) 
(in English) We are planning to construct an image retrieval system using FPGA. For designing this system, we developed a prototype system, which retrieves the image having maximum number of matched local image features with a query image. The reason why we don’t use Bag of Features (BoF) is that codebook referencing may consume considerable time and computational resources on FPGA. For this purpose, the local features describing a stored image should satisfy the following conditions: 1) they should have strong discrimination power from other images, 2) they should be robust against observation distortions including rotation, scaling, and so on. In order to maximize the number of stored images, the number of local features describing a stored image should be minimized. For selecting such “good local features” from all local features, we propose a method based on Diverse Density. In the experiment, we confine the number of local features describing a single image to 10, and our method outperforms other local feature selection methods.
Keyword (in Japanese) (See Japanese page) 
(in English) image retrieval / local feature selection / Diverse Density / discrimination power / robustness against distortions / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 385, PRMU2012-91, pp. 87-92, Jan. 2013.
Paper # PRMU2012-91 
Date of Issue 2013-01-16 (PRMU, MVE) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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)
Download PDF PRMU2012-91 MVE2012-56

Conference Information
Committee PRMU MVE IPSJ-CVIM  
Conference Date 2013-01-23 - 2013-01-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2013-01-PRMU-MVE-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Keypoint selection based on Diverse Density for image retrieval 
Sub Title (in English)  
Keyword(1) image retrieval  
Keyword(2) local feature selection  
Keyword(3) Diverse Density  
Keyword(4) discrimination power  
Keyword(5) robustness against distortions  
Keyword(6)  
Keyword(7)  
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1st Author's Name Keita Yuasa  
1st Author's Affiliation Wakayama University (Wakayama Univ.)
2nd Author's Name Toshikazu Wada  
2nd Author's Affiliation Wakayama University (Wakayama Univ.)
3rd Author's Name Hiroshi Oike  
3rd Author's Affiliation Wakayama University (Wakayama Univ.)
4th Author's Name Jun Sakata  
4th Author's Affiliation Wakayama University (Wakayama Univ.)
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Speaker Author-1 
Date Time 2013-01-23 11:40:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2012-91, MVE2012-56 
Volume (vol) vol.112 
Number (no) no.385(PRMU), no.386(MVE) 
Page pp.87-92 
#Pages 6 
Date of Issue 2013-01-16 (PRMU, MVE) 

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