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<p>To ensure the effective preservation and evolution of traditional painting art, it is imperative to integrate contemporary art design concepts and methodologies. This paper focuses on the reconstruction of traditional Chinese painting elements, distilling and adapting its pattern, ink, and calligraphy components into a modern design framework. Specifically, the features of the pattern elements are identified and transformed using a feature point extraction algorithm combined with K-Means clustering. For ink and calligraphy elements, a fusion of self-attention mechanisms and Generative Adversarial Networks (GANs) facilitates the stylistic migration and conversion of these elements through the construction of distinct discriminators. Subsequent evaluation of the transformed painting elements utilized Importance-Performance Analysis (IPA) within a contemporary design context, aiming to assess the practical value of traditional painting elements in modern design. The findings indicate that the clarity of information (C1) and its accuracy and efficacy (C3) in the context of contemporary design achieved importance scores exceeding 3.6. Furthermore, the satisfaction scores for modeling (C10), color (C11), and imagery (C12) each surpassed 3.5, denoting a high level of satisfaction in the IPA of their conversion. The study enriches traditional artistic methods and conceptualizations, thereby enhancing their cultural depth and significance.</p>
</abstract>

To ensure the effective preservation and evolution of traditional painting art, it is imperative to integrate contemporary art design concepts and methodologies. This paper focuses on the reconstruction of traditional Chinese painting elements, distilling and adapting its pattern, ink, and calligraphy components into a modern design framework. Specifically, the features of the pattern elements are identified and transformed using a feature point extraction algorithm combined with K-Means clustering. For ink and calligraphy elements, a fusion of self-attention mechanisms and Generative Adversarial Networks (GANs) facilitates the stylistic migration and conversion of these elements through the construction of distinct discriminators. Subsequent evaluation of the transformed painting elements utilized Importance-Performance Analysis (IPA) within a contemporary design context, aiming to assess the practical value of traditional painting elements in modern design. The findings indicate that the clarity of information (C1) and its accuracy and efficacy (C3) in the context of contemporary design achieved importance scores exceeding 3.6. Furthermore, the satisfaction scores for modeling (C10), color (C11), and imagery (C12) each surpassed 3.5, denoting a high level of satisfaction in the IPA of their conversion. The study enriches traditional artistic methods and conceptualizations, thereby enhancing their cultural depth and significance.

The Transformation of Traditional Chinese Painting Elements in Modern Design Contexts

Applied Mathematics and Nonlinear Sciences

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

To ensure the effective preservation and evolution of traditional painting art, it is imperative to integrate contemporary art design concepts and...

The Transformation of Traditional Chinese Painting Elements in Modern Design Contexts

Published Online: Jul 05, 2024

Page range: -

Received: Mar 08, 2024

Accepted: May 25, 2024

DOI: https://doi.org/10.2478/amns-2024-1640

Keywords
K-means clustering algorithm, GAN network, Style migration, IPA analysis, Traditional painting elements

© 2024 Fan Ji, published by Sciendo

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

The Transformation of Traditional Chinese Painting Elements in Modern Design Contexts

Published Online: Jul 05, 2024

Page range: -

Received: Mar 08, 2024

Accepted: May 25, 2024

DOI: https://doi.org/10.2478/amns-2024-1640

KeywordsK-means clustering algorithm, GAN network, Style migration, IPA analysis, Traditional painting elements

© 2024 Fan Ji, published by Sciendo

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

Keywords
K-means clustering algorithm, GAN network, Style migration, IPA analysis, Traditional painting elements