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Volume: 34 | Article ID: HVEI-189
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INDeeD: Identical and disparate feature decomposition from multi-label data
Tserendorj Adiya
  Tserendorj Adiya
Affiliation
Kyung Hee University, Republic of Korea
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Seungkyu Lee
  Seungkyu Lee
Affiliation
Kyung Hee University, Republic of Korea
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  DOI :  10.2352/EI.2022.34.11.HVEI-189  Published Online :  January 2022
Abstract
Abstract

We propose Identical and Disparate Feature Decomposition (INDeeD) from multi-label data that explicitly learn the characteristics of individual label.

Journal Title : Electronic Imaging
Publisher Name : Society for Imaging Science and Technology
Publisher Location : IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA
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 DOI 10.2352/EI.2022.34.11.HVEI-189
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Tserendorj Adiya, Seungkyu Lee, "INDeeD: Identical and disparate feature decomposition from multi-label data"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2022,  pp 189-1 - 189-8,  https://doi.org/10.2352/EI.2022.34.11.HVEI-189

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  Copyright statement 
Copyright © Society for Imaging Science and Technology 2022
ei
Electronic Imaging
2470-1173
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA
10.2352/EI.2022.34.11.HVEI-189
HVEI-189
Article
INDeeD: Identical and disparate feature decomposition from multi-label data
AdiyaTserendorj
Kyung Hee University, Republic of Korea
LeeSeungkyu
Kyung Hee University, Republic of Korea
Abstract
We propose Identical and Disparate Feature Decomposition (INDeeD) from multi-label data that explicitly learn the characteristics of individual label.
16012022
34
HVEI
11
189-1
189-8
© 2022, Society for Imaging Science and Technology
2022
Human vision Multi-label image classification