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

Abstract This study presents a machine learning-based approach for analyzing and classifying modern and contemporary Korean paintings using image data. The model leverages a visual-language multimodal model for efficient visual feature extraction and employs a multi-layered image analysis to capture detailed formal characteristics. Color features are extracted through analyses of various color spaces, while texture information is quantified within the texture feature space. The extracted feature vectors are analyzed and visualized through clustering, achieving an artist classification accuracy of 82.4%. Representative images from each artist cluster effectively encapsulate and highlight distinctive color and textural characteristics. Additionally, image captioning techniques were applied to generate textual descriptions of the representative images, successfully translating visual features into descriptive text. The findings confirm that machine learning-based image analysis offers an effective and objective methodology for identifying and classifying the unique characteristics of modern and contemporary Korean paintings.