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Paper Abstract and Keywords
Presentation 2018-10-25 11:10
Improvement of Classification Accuracy for Imbalanced Training Data by CasNet
Takuro Oki, Ryusuke Miyamoto (Meiji Univ.) SIS2018-13
Abstract (in Japanese) (See Japanese page) 
(in English) Imbalanced samples composed of limited number of positive samples corresponding to objects and huge number of negative samples extracted from background regions reduces the accuracy of visual object detection. To solve this problem, this paper proposes a novel convolutional neural network named "CasNet". CasNet introduces cascade structure that is used for rapid and accurate object detector in order to reduce the number of negative. The CasNet become a cascade stage when it is attached to a layer of existing convolutinoal neural networks to construct cascaded classifier. Each stage composed of a CasNet peforms two-class classification to reject easy negatives corresponding to background regions. By this early rejection of easy negatives, a main network can be trained to classify more complex samples. Experimental results using a dataset created from the PASCAL VOC2012 dataset showed that higher accuracy was obtained at less training iterations if CasNets were attached to VGG16 appropriately.
Keyword (in Japanese) (See Japanese page) 
(in English) Visual object detection / Convolutional neural network / Data imbalance problem / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 264, SIS2018-13, pp. 19-24, Oct. 2018.
Paper # SIS2018-13 
Date of Issue 2018-10-18 (SIS) 
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 SIS2018-13

Conference Information
Committee SIS ITE-BCT  
Conference Date 2018-10-25 - 2018-10-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto University Clock Tower Centennial Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) System Implementation Technology, Short Range Wireless Systems, Smart Multimedia Systems, Broadcasting Technology, etc. 
Paper Information
Registration To SIS 
Conference Code 2018-10-SIS-BCT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improvement of Classification Accuracy for Imbalanced Training Data by CasNet 
Sub Title (in English)  
Keyword(1) Visual object detection  
Keyword(2) Convolutional neural network  
Keyword(3) Data imbalance problem  
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1st Author's Name Takuro Oki  
1st Author's Affiliation Meiji University (Meiji Univ.)
2nd Author's Name Ryusuke Miyamoto  
2nd Author's Affiliation Meiji University (Meiji Univ.)
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Speaker Author-1 
Date Time 2018-10-25 11:10:00 
Presentation Time 20 minutes 
Registration for SIS 
Paper # SIS2018-13 
Volume (vol) vol.118 
Number (no) no.264 
Page pp.19-24 
#Pages 6 
Date of Issue 2018-10-18 (SIS) 

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