Identifying Relationships and Classifying Western-Style Paintings: Machine Learning Approaches for Artworks by Western Artists and Meiji-Era Japanese Artists

Abstract

Many Western-style paintings by Japanese artists in the early 1900s, though maintaining a unique quality, were greatly inspired by the works of Western artists. In this paper, we employ machine learning to identify relationships and classify the works of Japanese and Western artists. The relationships are of significant interest to numerous art historians, as they can reveal how Western art was introduced to Japan. Historically, art historians have manually annotated these correspondences, which is a time-consuming and labor-intensive process. In this paper, we introduce a new method for finding correspondences between related artworks by comparing their overall outline information. This technique is based on Siamese neural networks (SNNs) and a self-supervised learning approach. Additionally, we have compiled a dataset of illustrations from Japanese artists such as Seiki Kuroda and Western artists such as Raphaël Collin, complete with correspondence annotations. On the other hand, to exhibit the unique quality of works by Japanese artists, we demonstrate that machine learning can classify between artworks created by Japanese artists and those created by Western artists.