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<p>Various exquisite graphic designs that can be seen everywhere beautify people’s vision and influence people’s visual aesthetics subtly. This paper starts with the visual aesthetic elements of graphic design and discusses the construction of visual aesthetic element systems in graphic design by analyzing the composition elements and aesthetic characteristics. Secondly, it proposes to use the biological visual perceptual machine model to process information through two visual pathways in a hierarchical manner. The features of graphic design images are expressed, stored, and extracted using the visual information model. Finally, the effectiveness of the bio-visual perceptual model applied to the construction of visual aesthetic elements of graphic design is verified by the Caltech5 database, Caltech256 database, and Scene15 database. The results show that the classification accuracy of the bio-visual perceptron model is 94.28%, 91.36%, and 99.75% in Caltech5, Caltech256, and Scene15 databases, respectively, which is 23.12% higher than the accuracy of other neural network models on average. The bio-visual perceptron model proposed in this paper can accurately detect the elemental features in complex graphic design, which provides a new idea and method for rapid detection and recognition for graphic design workers.</p>
</abstract>

Various exquisite graphic designs that can be seen everywhere beautify people’s vision and influence people’s visual aesthetics subtly. This paper starts with the visual aesthetic elements of graphic design and discusses the construction of visual aesthetic element systems in graphic design by analyzing the composition elements and aesthetic characteristics. Secondly, it proposes to use the biological visual perceptual machine model to process information through two visual pathways in a hierarchical manner. The features of graphic design images are expressed, stored, and extracted using the visual information model. Finally, the effectiveness of the bio-visual perceptual model applied to the construction of visual aesthetic elements of graphic design is verified by the Caltech5 database, Caltech256 database, and Scene15 database. The results show that the classification accuracy of the bio-visual perceptron model is 94.28%, 91.36%, and 99.75% in Caltech5, Caltech256, and Scene15 databases, respectively, which is 23.12% higher than the accuracy of other neural network models on average. The bio-visual perceptron model proposed in this paper can accurately detect the elemental features in complex graphic design, which provides a new idea and method for rapid detection and recognition for graphic design workers.

The construction of visual aesthetic element system in graphic design based on big data

Applied Mathematics and Nonlinear Sciences

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

Various exquisite graphic designs that can be seen everywhere beautify people’s vision and influence people’s visual aesthetics subtly. This paper starts with...

The construction of visual aesthetic element system in graphic design based on big data

Published Online: Sep 09, 2023

Page range: -

Received: Dec 01, 2022

Accepted: Mar 20, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00315

Keywords
Graphic design, Visual aesthetics, Bio-visual perceptual machine model, Visual information model, Database

© 2023 Jing Shen et al., published by Sciendo

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

The construction of visual aesthetic element system in graphic design based on big data

Published Online: Sep 09, 2023

Page range: -

Received: Dec 01, 2022

Accepted: Mar 20, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00315

KeywordsGraphic design, Visual aesthetics, Bio-visual perceptual machine model, Visual information model, Database

© 2023 Jing Shen et al., published by Sciendo

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

Keywords
Graphic design, Visual aesthetics, Bio-visual perceptual machine model, Visual information model, Database