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<p>In the face of people’s growing spiritual civilization needs, the application of traditional art colors in modern visual communication design has become a hot spot of attention. In this paper, a model for extracting traditional art color features is constructed to provide a methodology and theory for applying traditional art color in modern visual communication design. In the model construction, the max-min clustering algorithm is proposed to achieve color segmentation, measure the distance between the clustering center and the samples, and improve computing speed. Based on the K-means algorithm, the initial clustering center selection and the number of class clusters are improved, and the color feature extraction method is optimized to improve the accuracy of color extraction. In the performance experiments of the model, the All, Noncc, and Disc parameters of this paper’s color segmentation algorithm are lower than those of other algorithms in color segmentation of different pictures, and the performance is good. The accuracy of the color feature extraction test in different color spaces is close to 100%, the average verification accuracy is as high as 99.8%, and it takes less time. The aesthetic, originality, symbolism, and spirituality evaluation indexes of the poster work “Lead,” designed by combining the model of this paper, have an average score of more than 4, which reaches a good level.</p>
</abstract>

In the face of people’s growing spiritual civilization needs, the application of traditional art colors in modern visual communication design has become a hot spot of attention. In this paper, a model for extracting traditional art color features is constructed to provide a methodology and theory for applying traditional art color in modern visual communication design. In the model construction, the max-min clustering algorithm is proposed to achieve color segmentation, measure the distance between the clustering center and the samples, and improve computing speed. Based on the K-means algorithm, the initial clustering center selection and the number of class clusters are improved, and the color feature extraction method is optimized to improve the accuracy of color extraction. In the performance experiments of the model, the All, Noncc, and Disc parameters of this paper’s color segmentation algorithm are lower than those of other algorithms in color segmentation of different pictures, and the performance is good. The accuracy of the color feature extraction test in different color spaces is close to 100%, the average verification accuracy is as high as 99.8%, and it takes less time. The aesthetic, originality, symbolism, and spirituality evaluation indexes of the poster work “Lead,” designed by combining the model of this paper, have an average score of more than 4, which reaches a good level.

Discussion on the use of traditional art colors in modern visual communication design

Weifang University of Science and Technology

Applied Mathematics and Nonlinear Sciences

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

In the face of people’s growing spiritual civilization needs, the application of traditional art colors in modern visual communication design has become a hot...

Discussion on the use of traditional art colors in modern visual communication design

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 05 apr 2024

Accettato: 30 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2114

Parole chiave
max-min clustering algorithm, Clustering center, K-means algorithm, Traditional art colors

© 2024 Ying Lu, published by Sciendo

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

Discussion on the use of traditional art colors in modern visual communication design

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 05 apr 2024

Accettato: 30 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2114

Parole chiavemax-min clustering algorithm, Clustering center, K-means algorithm, Traditional art colors

© 2024 Ying Lu, published by Sciendo

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

Parole chiave
max-min clustering algorithm, Clustering center, K-means algorithm, Traditional art colors