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Under the impetus of new concepts and new thinking, digital media art is also born. This paper discusses the integration of digital media in oil painting teaching in colleges and universities and constructs a resource library of painting images created by college and university students. The original RGB image is converted to HSV mode, and the college oil painting images are classified into styles based on color entropy. Drawing on the category balanced intersectionality loss function commonly used in edge extraction networks and designing an objective evaluation index based on the Adain network model for comparing the stylistic categorization ability of oil painting image translation networks. Finally, a generalization experiment was conducted on oil painting teaching painting images in colleges and universities through the Gallerix dataset. The results show that in the 2566-dimensional vector data distribution extracted from the oil painting resource base of art majors in H colleges and universities, for example, with <italic>dc</italic> = 0.05 and <italic>dc</italic> = 0.1, both sets of parameters find the correct clustering centers, which greatly enhances the efficiency of oil painting creation by college and university students. The method can promote the further development of artificial intelligence technology in the field of art creation and evaluation.
</abstract>

Under the impetus of new concepts and new thinking, digital media art is also born. This paper discusses the integration of digital media in oil painting teaching in colleges and universities and constructs a resource library of painting images created by college and university students. The original RGB image is converted to HSV mode, and the college oil painting images are classified into styles based on color entropy. Drawing on the category balanced intersectionality loss function commonly used in edge extraction networks and designing an objective evaluation index based on the Adain network model for comparing the stylistic categorization ability of oil painting image translation networks. Finally, a generalization experiment was conducted on oil painting teaching painting images in colleges and universities through the Gallerix dataset. The results show that in the 2566-dimensional vector data distribution extracted from the oil painting resource base of art majors in H colleges and universities, for example, with dc = 0.05 and dc = 0.1, both sets of parameters find the correct clustering centers, which greatly enhances the efficiency of oil painting creation by college and university students. The method can promote the further development of artificial intelligence technology in the field of art creation and evaluation.

Research on the Integration of Digital Media and Oil Painting Teaching in Colleges and Universities in the Era of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under the impetus of new concepts and new thinking, digital media art is also born. This paper discusses the integration of digital media in oil painting...

Research on the Integration of Digital Media and Oil Painting Teaching in Colleges and Universities in the Era of Artificial Intelligence

Online veröffentlicht: 20. Dez. 2023

Seitenbereich: -

Eingereicht: 02. Feb. 2023

Akzeptiert: 30. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01574

Schlüsselwörter
Color entropy, Edge extraction network, Loss function, Adain network model, Gallerix dataset

© 2023 Lingyu Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Integration of Digital Media and Oil Painting Teaching in Colleges and Universities in the Era of Artificial Intelligence

Online veröffentlicht: 20. Dez. 2023

Seitenbereich: -

Eingereicht: 02. Feb. 2023

Akzeptiert: 30. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01574

SchlüsselwörterColor entropy, Edge extraction network, Loss function, Adain network model, Gallerix dataset

© 2023 Lingyu Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Color entropy, Edge extraction network, Loss function, Adain network model, Gallerix dataset