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Paper Abstract and Keywords
Presentation 2020-03-17 10:45
Semi-Supervised Temporal Segmentation of Industrial Operation Video based on Deep Metric Learning
Daiki Kawamori, Kazuaki Nakamura, Naoko Nitta, Noboru Babaguchi (Osaka Univ.) PRMU2019-92
Abstract (in Japanese) (See Japanese page) 
(in English) Today, cameras are often installed in many production sites for various purposes.
However, untrimmed raw videos captured by the cameras are hard to use.
Hence, it is desired to automatically segment the videos along the time axis and recognize which kind of operation is performed in each segment.
We call this task ``temporal segmentation,'' which is useful for making an operational record and building a new plan.
To achieve high performance of temporal segmentation, we have to use effective video features.
Such features can hardly be obtained by unsupervised learning,
whereas supervised learning has a drawback that collecting a lot of training data is labor-intensive.
From these backgrounds, in this paper, we propose a method of obtaining effective features based on semi-supervised distance metric learning,
under the assumption that only a few frames in input industrial operation videos are labeled and given as training data.
To achieve high performance, the proposed method automatically build a tree structure that represents hierarchal relationship between class labels,
and separately obtain an effective feature for each branch in the tree.
In our experimental results, we achieved the temporal segmentation performance of 0.956 on the F measure, even when less than 3% of all frames in the input videos are labeled.
Keyword (in Japanese) (See Japanese page) 
(in English) industrial operation video / deep metric learning / temporal segmentation / semi-supervised clustering / label hierarchy tree / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 481, PRMU2019-92, pp. 139-144, March 2020.
Paper # PRMU2019-92 
Date of Issue 2020-03-09 (PRMU) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 PRMU2019-92

Conference Information
Committee PRMU IPSJ-CVIM  
Conference Date 2020-03-16 - 2020-03-17 
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 2020-03-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Semi-Supervised Temporal Segmentation of Industrial Operation Video based on Deep Metric Learning 
Sub Title (in English)  
Keyword(1) industrial operation video  
Keyword(2) deep metric learning  
Keyword(3) temporal segmentation  
Keyword(4) semi-supervised clustering  
Keyword(5) label hierarchy tree  
1st Author's Name Daiki Kawamori  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Kazuaki Nakamura  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Naoko Nitta  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Noboru Babaguchi  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Date Time 2020-03-17 10:45:00 
Presentation Time 15 
Registration for PRMU 
Paper # IEICE-PRMU2019-92 
Volume (vol) IEICE-119 
Number (no) no.481 
Page pp.139-144 
#Pages IEICE-6 
Date of Issue IEICE-PRMU-2020-03-09 

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