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<p>This study presents a machine learning framework for analyzing and clustering modern and contemporary Korean paintings based on image data. A pretrained vision–language architecture combined with multi-layered analysis was used to efficiently extract detailed formal characteristics, including color features from multiple spaces and quantified texture. The extracted feature vectors are clustered and evaluated under majority-label assignment, achieving 82.4% overall accuracy, outperforming single-feature baselines (RGB 82.0%, HSV 81.3%, histogram 51.0%, LBP 68.8%, and GLCM 73.7%). The proposed method achieves higher per-artist precision, better boundary-case discrimination, and greater robustness for low-sample categories. Representative images from artist clusters encapsulate unique color and texture. Analysis was extended using automatic image captioning and zero-shot style assignment via matching image and text embeddings. The findings demonstrate that machine learning–based image analysis provides an effective and objective methodology for identifying and distinguishing visual characteristics of modern and contemporary Korean paintings, offering a quantitative approach to art-historical interpretation.</p>

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Toward enhanced unsupervised clustering of 20th century Korean paintings via multimodal features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Seohyun Baek,
  • So-Jeong Park,
  • So-Eun Park,
  • You-Min Im,
  • Jongwon Choi,
  • Bo-A Rhee

摘要

This study presents a machine learning framework for analyzing and clustering modern and contemporary Korean paintings based on image data. A pretrained vision–language architecture combined with multi-layered analysis was used to efficiently extract detailed formal characteristics, including color features from multiple spaces and quantified texture. The extracted feature vectors are clustered and evaluated under majority-label assignment, achieving 82.4% overall accuracy, outperforming single-feature baselines (RGB 82.0%, HSV 81.3%, histogram 51.0%, LBP 68.8%, and GLCM 73.7%). The proposed method achieves higher per-artist precision, better boundary-case discrimination, and greater robustness for low-sample categories. Representative images from artist clusters encapsulate unique color and texture. Analysis was extended using automatic image captioning and zero-shot style assignment via matching image and text embeddings. The findings demonstrate that machine learning–based image analysis provides an effective and objective methodology for identifying and distinguishing visual characteristics of modern and contemporary Korean paintings, offering a quantitative approach to art-historical interpretation.