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Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation

Asanobu Kitamoto
Chikahiko Suzuki
Tarin Clanuwat
International Conference on Computational Creativity (2021), pp. 300-308 (to appear)
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The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses on the object and style like other artwork researches. Such study has benefited from the renewed interest by the machine learning community in culturally important topics, leading to interdisciplinary works using data-driven and machine learning-based methods. These works include collections of images, quantitative approaches, and machine learning-based creativities. They, however, have several drawbacks, and it remains challenging to integrate these works for a comprehensive view. To bridge this gap, we propose a holistic approach leveraging large scale data and machine learning models. We first present a large-scale Ukiyo-e dataset with coherent semantic labels and geometric annotations, then show its value in a quantitative study of Ukiyo-e paintings' object using these labels and annotations. We further demonstrate the machine learning-based methods could help style study through soft color decomposition of Ukiyo-e, and provides joint insightings into object and style by separating sketches and colors.

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