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Paper Abstract and Keywords
Presentation 2025-01-22 13:40
Proximal sampling of training data in DN4C
Kizuki Yamada, Toshikazu Wada, Koji Kamma (Wakayama Univ) PRMU2024-44
Abstract (in Japanese) (See Japanese page) 
(in English) DNN based image segmentation is widely used in various fields. However, DNN training requires training data and corresponding annotation data. Training data collection, annotation data creation, and the training of DNNs using these data take a long time. One solution to this problem is to perform image segmentation interactively. DN4C is such a interactive image segmentation method combining DNN and Nearest Neighbor Classifier. In DN4C, the loss function is computed from pairs of data sampled from two sets of data in the feature space. The loss function is minimized so that the same label generates an attraction and the different label generates a repulsion, both of them are stronger at short distances and weaker at longer distances. For efficient learning, it is therefore necessary to sample closely located data pairs. In this report, we propose a proximity sampling method using coarsely divided feature space into coarse cubes, i.e., sampling within the divided cubes can produce proximal data pairs. We examined the effectiveness of the proposed method through experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) Spatial Segmentation / Fixed Segmentation / octree / DN4C / Image Segmentation / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 336, PRMU2024-44, pp. 61-66, Jan. 2025.
Paper # PRMU2024-44 
Date of Issue 2025-01-14 (PRMU) 
ISSN 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 PRMU2024-44

Conference Information
Committee PRMU IPSJ-CVIM VRSJ-SIG-MR MVE  
Conference Date 2025-01-21 - 2025-01-22 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2025-01-PRMU-CVIM-SIG-MR-MVE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Proximal sampling of training data in DN4C 
Sub Title (in English)  
Keyword(1) Spatial Segmentation  
Keyword(2) Fixed Segmentation  
Keyword(3) octree  
Keyword(4) DN4C  
Keyword(5) Image Segmentation  
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1st Author's Name Kizuki Yamada  
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 Koji Kamma  
3rd Author's Affiliation Wakayama University (Wakayama Univ)
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Speaker Author-1 
Date Time 2025-01-22 13:40:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2024-44 
Volume (vol) vol.124 
Number (no) no.336 
Page pp.61-66 
#Pages 6 
Date of Issue 2025-01-14 (PRMU) 

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