Learning of art style using AI and its evaluation based on psychological experiments Online publication date: Mon, 23-Jan-2023
by Mai Cong Hung; Ryohei Nakatsu; Naoko Tosa; Takashi Kusumi
International Journal of Arts and Technology (IJART), Vol. 14, No. 3, 2022
Abstract: Generative adversarial networks (GANs) are AI technology that can achieve transformation between two image sets. Using GANs, the authors carried out a comparison among several artwork sets with four art styles: Western figurative painting set, Western abstract painting set, Chinese figurative painting set, and abstract image set created by one of the authors. The transformation from a flower photo set to each of these image sets was carried out using GAN, and four image sets, for which their original artworks and art genres were anonymised, were obtained. A psychological experiment was conducted by asking subjects to fill in questionnaires. By analysing the results, the authors found that abstract paintings and figurative paintings are judged to be different and also figurative paintings in the West and East were thought to be similar. These results show that AI can work as an analysis tool to investigate differences among artworks and art genres.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Arts and Technology (IJART):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook