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Paper Abstract and Keywords
Presentation 2018-03-18 16:45
Evaluation of the Shot Boundary Detection Method based on Unsupervised Learning from Video Big Data
Norio Katayama, Hiroshi Mo, Shin'ichi Satoh (NII) BioX2017-53 PRMU2017-189
Abstract (in Japanese) (See Japanese page) 
(in English) Video data is a sequence of video frames and their temporal continuity
is an essential property of video stream. In this paper, we present a framework of constructing video continuity model from large-scale video archives with unsupervised learning. Our method estimates the similarity distribution of continuous frame pairs by applying simple assumption on the minimum duration of continuous video segments and then determines discontinuous frame pairs as outliers. In order to verify the validity of the obtained model, the model is applied to the shot boundary detection. The results of experimental evaluation demonstrate the feasibility and the effectiveness of our method.
Keyword (in Japanese) (See Japanese page) 
(in English) shot boundary detection / unsupervised learning / video continuity model / video archive / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 514, PRMU2017-189, pp. 103-108, March 2018.
Paper # PRMU2017-189 
Date of Issue 2018-03-11 (BioX, PRMU) 
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 BioX2017-53 PRMU2017-189

Conference Information
Committee PRMU BioX  
Conference Date 2018-03-18 - 2018-03-19 
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 2018-03-PRMU-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of the Shot Boundary Detection Method based on Unsupervised Learning from Video Big Data 
Sub Title (in English)  
Keyword(1) shot boundary detection  
Keyword(2) unsupervised learning  
Keyword(3) video continuity model  
Keyword(4) video archive  
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1st Author's Name Norio Katayama  
1st Author's Affiliation National Institute of Informatics (NII)
2nd Author's Name Hiroshi Mo  
2nd Author's Affiliation National Institute of Informatics (NII)
3rd Author's Name Shin'ichi Satoh  
3rd Author's Affiliation National Institute of Informatics (NII)
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Speaker Author-1 
Date Time 2018-03-18 16:45:00 
Presentation Time 25 minutes 
Registration for PRMU 
Paper # BioX2017-53, PRMU2017-189 
Volume (vol) vol.117 
Number (no) no.513(BioX), no.514(PRMU) 
Page pp.103-108 
#Pages 6 
Date of Issue 2018-03-11 (BioX, PRMU) 

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