Color Visual Expression in Product Packaging Design Based on Feature Fusion Network

To improve the effectiveness of visual representation schemes for product packaging colors and enhance the competitiveness and attractiveness of products, this study proposed to construct a color intent dataset based on multiple fusion algorithms. On this basis, a product packaging color visual expr...

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Vydané v:Journal of advanced computational intelligence and intelligent informatics Ročník 29; číslo 6; s. 1292 - 1304
Hlavný autor: Liu, Zemei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Tokyo Fuji Technology Press Co. Ltd 20.11.2025
Predmet:
Accuracy
Color
Computer vision
Consumption
Convolution
Datasets
Deep learning
Empirical analysis
Food
Food packaging
Generative adversarial networks
Neural networks
Packaging design
Roads & highways
Semantics
User needs
ISSN:1343-0130, 1883-8014
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Abstract To improve the effectiveness of visual representation schemes for product packaging colors and enhance the competitiveness and attractiveness of products, this study proposed to construct a color intent dataset based on multiple fusion algorithms. On this basis, a product packaging color visual expression model based on a conditional deep convolution generative adversarial network was constructed. The empirical analysis of the model showed that its accuracy was 94.36% and the running time was 50.2 seconds, indicating better performance than the comparative models. In addition, this study also rated its satisfaction and found that the average satisfaction score of the model was 9.2, higher than the other comparative models. The proposed visual expression model of product packaging color based on conditional depth convolution generative adversarial network had better accuracy and computing speed performance than other comparison models. The color scheme provided by this model better met user needs compared to other models and had great potential for application, providing a certain theoretical basis for product packaging color design.
AbstractList To improve the effectiveness of visual representation schemes for product packaging colors and enhance the competitiveness and attractiveness of products, this study proposed to construct a color intent dataset based on multiple fusion algorithms. On this basis, a product packaging color visual expression model based on a conditional deep convolution generative adversarial network was constructed. The empirical analysis of the model showed that its accuracy was 94.36% and the running time was 50.2 seconds, indicating better performance than the comparative models. In addition, this study also rated its satisfaction and found that the average satisfaction score of the model was 9.2, higher than the other comparative models. The proposed visual expression model of product packaging color based on conditional depth convolution generative adversarial network had better accuracy and computing speed performance than other comparison models. The color scheme provided by this model better met user needs compared to other models and had great potential for application, providing a certain theoretical basis for product packaging color design.
Author Liu, Zemei
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SubjectTerms Accuracy
Color
Computer vision
Consumption
Convolution
Datasets
Deep learning
Empirical analysis
Food
Food packaging
Generative adversarial networks
Neural networks
Packaging design
Roads & highways
Semantics
User needs
Title Color Visual Expression in Product Packaging Design Based on Feature Fusion Network
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