Detail Focused Image Classifier Model for Traditional Images

전통문화 이미지를 위한 세부 자질 주목형 이미지 자동 분석기

  • Kim, Kuekyeng (Department of Computer Science and Engineering, Korea University) ;
  • Hur, Yuna (Department of Computer Science and Engineering, Korea University) ;
  • Kim, Gyeongmin (Department of Computer Science and Engineering, Korea University) ;
  • Yu, Wonhee (Department of Computer Science and Engineering, Korea University) ;
  • Lim, Heuiseok (Department of Computer Science and Engineering, Korea University)
  • Received : 2017.10.20
  • Accepted : 2017.12.20
  • Published : 2017.12.28


As accessibility toward traditional cultural contents drops compared to its increase in production, the need for higher accessibility for continued management and research to exist. For this, this paper introduces an image classifier model for traditional images based on artificial neural networks, which converts the input image's features into a vector space and by utilizing a RNN based model it recognizes and compares the details of the input which enables the classification of traditional images. This enables the classifiers to classify similarly looking traditional images more precisely by focusing on the details. For the training of this model, a wide range of images were arranged and collected based on the format of the Korean information culture field, which contributes to other researches related to the fields of using traditional cultural images. Also, this research contributes to the further activation of demand, supply, and researches related to traditional culture.


Grant : 2017. 전통문화 융복합 지원을 위한 지능형 검색 플랫폼 구축

Supported by : 한국콘텐츠진흥원


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