Disruptive Transformation of Artworks in Master-Disciple Relationships: The Case of Ukiyo-e Artworks

TL;DR

This study applies machine learning to quantitatively analyze the creativity and evolution of Ukiyo-e artworks, revealing cultural trends and stylistic segmentation over time using a large dataset of high-resolution images.

Contribution

It introduces a novel quantitative approach to assess creativity in Eastern art, specifically Ukiyo-e, using network-based metrics on a large image dataset.

Findings

Ukiyo-e's overall creativity declined with cultural maturation.

Stylistic segmentation increased, maintaining high creativity levels.

Provides new insights into the evolution of Eastern art through quantitative analysis.

Abstract

Artwork research has long relied on human sensibility and subjective judgment, but recent developments in machine learning have enabled the quantitative assessment of features that humans could not discover. In Western paintings, comprehensive analyses have been conducted from various perspectives in conjunction with large databases, but such extensive analysis has not been sufficiently conducted for Eastern paintings. Then, we focus on Ukiyo-e, a traditional Japanese art form, as a case study of Eastern paintings, and conduct a quantitative analysis of creativity in works of art using 11,000 high-resolution images. This involves using the concept of calculating creativity from networks to analyze both the creativity of the artwork and that of the artists. As a result, In terms of Ukiyo-e as a whole, it was found that the creativity of its appearance has declined with the maturation of culture, but in terms of style, it has become more segmented with the maturation of culture and has maintained a high level of creativity. This not only provides new insights into the study of Ukiyo-e but also shows how Ukiyo-e has evolved within the ongoing cultural history, playing a culturally significant role in the analysis of Eastern art.