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<p>Exploring the effective application of color elements in graphic design based on a convolutional neural network model is beneficial to promote the innovative development of the graphic design. Starting from the convolutional neural network, this paper defines the quantification of geometric features of graphic images by convolutional operation and pooling operation and uses pixel and spatial distribution for color element extraction. The global color features and local color features are used for color matching perception feature quantification, and then a graphic image design feature model is constructed, and the application analysis of color elements is carried out for this model. From the primary design color, the average selection rate of this paper’s model is 41.58%, which is 13.4% and 9.6% higher than that of the DCGAN and PLS-MLP models, respectively. From the keyword color quality generation, the color design results of graphic images have significant differences (<italic>p</italic> &lt; 0.05). This indicates that the use of CNN-based graphic image design feature models can realize the effective application of color elements and also provides a new research direction for intelligent graphic design.</p>
</abstract>

Exploring the effective application of color elements in graphic design based on a convolutional neural network model is beneficial to promote the innovative development of the graphic design. Starting from the convolutional neural network, this paper defines the quantification of geometric features of graphic images by convolutional operation and pooling operation and uses pixel and spatial distribution for color element extraction. The global color features and local color features are used for color matching perception feature quantification, and then a graphic image design feature model is constructed, and the application analysis of color elements is carried out for this model. From the primary design color, the average selection rate of this paper’s model is 41.58%, which is 13.4% and 9.6% higher than that of the DCGAN and PLS-MLP models, respectively. From the keyword color quality generation, the color design results of graphic images have significant differences (p < 0.05). This indicates that the use of CNN-based graphic image design feature models can realize the effective application of color elements and also provides a new research direction for intelligent graphic design.

The use of color elements in graphic design based on convolutional neural network model

Applied Mathematics and Nonlinear Sciences

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

Exploring the effective application of color elements in graphic design based on a convolutional neural network model is beneficial to promote the innovative...

The use of color elements in graphic design based on convolutional neural network model

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 29. Okt. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00536

Schlüsselwörter
Convolutional neural network, Graphic design, Color extraction, Color matching perception

© 2023 Shuhan Song, published by Sciendo

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

The use of color elements in graphic design based on convolutional neural network model

Online veröffentlicht: 09. Okt. 2023

Seitenbereich: -

Eingereicht: 29. Okt. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00536

SchlüsselwörterConvolutional neural network, Graphic design, Color extraction, Color matching perception

© 2023 Shuhan Song, published by Sciendo

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

Schlüsselwörter
Convolutional neural network, Graphic design, Color extraction, Color matching perception