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Identification of Korean Neo-Realism Artists Through CLIP-Based Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(12), pp.317-328
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 20, 2024
  • Accepted : December 23, 2024
  • Published : December 31, 2024

Seohyun Baek 1 So-Jeong Park 2 So-Eun Park 2 You-min Im 2 Bo-A Rhee 2 Jongwon Choi 2

1한국전자통신연구원
2중앙대학교

Accredited

ABSTRACT

This study proposes a methodology for analyzing artwork features and classifying artists using image data. Unlike prior research focused on Western artworks, it builds and utilizes a dataset of Korean Neo-Realism (i.e. Shinsasilpa) artists. The image encoder of the CLIP model transformed the semantic features of the artwork into a vector space and, by learning together with the textual description, more deeply understood the meaning of the image. Furthermore, hue information via RGB and HSV color space analysis, and texture characteristics through GLCM-based analysis. These features were integrated into representative feature vectors and analyzed with K-means clustering, achieving 87.4% classification accuracy. Visualization results demonstrated the model's effectiveness in identifying image similarities and accurately classifying artworks in an unsupervised learning context, while highlighting unique hue and texture characteristics of each cluster, revealing formal and artistic tendencies in artworks.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.