Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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A Study on the Classification Model of Minhwa Genre Based on Deep Learning

딥러닝 기반 민화 장르 분류 모델 연구

  • Yoon, Soorim (Dept. of Information and Communication Engineering, Dongguk University) ;
  • Lee, Young-Suk (Institute of Image and Cultural Contents, Dongguk University)
  • Received : 2022.08.16
  • Accepted : 2022.08.29
  • Published : 2022.10.31
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Abstract

This study proposes the classification model of Minhwa genre based on object detection of deep learning. To detect unique Korean traditional objects in Minhwa, we construct custom datasets by labeling images using object keywords in Minhwa DB. We train YOLOv5 models with custom datasets, and classify images using predicted object labels result, the output of model training. The algorithm consists of two classification steps: 1) according to the painting technique and 2) genre of Minhwa. Through classifying paintings using this algorithm on the Internet, it is expected that the correct information of Minhwa can be built and provided to users forward.

Keywords

Acknowledgement

This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (No. NRF-2021R1F1A1050452).

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