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http://dx.doi.org/10.9723/jksiis.2022.27.1.001

A deep learning model based on triplet losses for a similar child drawing selection algorithm  

Moon, Jiyu (한국원자력연구원 인공지능응용연구실, 이화여자대학교)
Kim, Min-Jong (한국원자력연구원 인공지능응용연구실)
Lee, Seong-Oak (주식회사 티엔에프에이아이)
Yu, Yonggyun (한국원자력연구원 인공지능응용연구실)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.1, 2022 , pp. 1-9 More about this Journal
Abstract
The goal of this paper is to create a deep learning model based on triplet loss for generating similar child drawing selection algorithms. To assess the similarity of children's drawings, the distance between feature vectors belonging to the same class should be close, and the distance between feature vectors belonging to different classes should be greater. Therefore, a similar child drawing selection algorithm was developed in this study by building a deep learning model combining Triplet Loss and residual network(ResNet), which has an advantage in measuring image similarity regardless of the number of classes. Finally, using this model's similar child drawing selection algorithm, the similarity between the target child drawing and the other drawings can be measured and drawings with a high similarity can be chosen.
Keywords
Image Similarity; Triplet Loss; Artificial Intelligence; Deep Learning; CNN; Child drawing analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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