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Paper Abstract and Keywords
Presentation 2010-09-05 09:30
Multiple Kernel Learning for Generic Object Recognition Using SIFT Gaussian Mixture Models
Nakamasa Inoue, Yusuke Kamishima, Koichi Shinoda, Sadaoki Furui (Tokyo Tech) PRMU2010-58 IBISML2010-30
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a statistical framework for generic object recognition using SIFT Gaussian mixture models (GMMs) and multiple kernel learning. We model SIFT features extracted from images by using GMMs. RBF kernels for multiple kernel learning are generated based on the distance between SIFT GMMs. In our experiments, we show which type of regularizer (e.g. l1,lp, elastic-net) is optimal for combining the kernels for different features, interest regions, and vocabulary sizes. Our best result obtained by using a single kernel was 41.0% in terms of Mean AP on the PASCAL VOC 2010 dataset, and it was improved to 50.5% by using the elastic-net multiple kernel learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Generic object recognition / SIFT GMMs / GMM supervector / Multiple kernel learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 187, PRMU2010-58, pp. 7-12, Sept. 2010.
Paper # PRMU2010-58 
Date of Issue 2010-08-29 (PRMU, IBISML) 
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)
Download PDF PRMU2010-58 IBISML2010-30

Conference Information
Committee IBISML PRMU IPSJ-CVIM  
Conference Date 2010-09-05 - 2010-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukuoka Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning and optimization for computer vision and pattern recognition, etc. 
Paper Information
Registration To PRMU 
Conference Code 2010-09-IBISML-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multiple Kernel Learning for Generic Object Recognition Using SIFT Gaussian Mixture Models 
Sub Title (in English)  
Keyword(1) Generic object recognition  
Keyword(2) SIFT GMMs  
Keyword(3) GMM supervector  
Keyword(4) Multiple kernel learning  
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1st Author's Name Nakamasa Inoue  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Yusuke Kamishima  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
3rd Author's Name Koichi Shinoda  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
4th Author's Name Sadaoki Furui  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
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Speaker Author-1 
Date Time 2010-09-05 09:30:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2010-58, IBISML2010-30 
Volume (vol) vol.110 
Number (no) no.187(PRMU), no.188(IBISML) 
Page pp.7-12 
#Pages 6 
Date of Issue 2010-08-29 (PRMU, IBISML) 

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