用图形神经网络和组合特征评估包装设计的美学质量评估
Xiaocen Guo1, Sijia Fu2, Di Zhu3
1College of Design, Hanyang University, Ansan-si, 15588, Korea.
Scientific reports
|October 15, 2025
概括
本研究引入了一种使用图形神经网络 (GNN) 和图像构成规则的新方法,以客观评估包装设计美学. 拟议的CGA-GNN模型显著提高了评估视觉通信设计质量的准确性和一致性.
科学领域:
- 视觉通信设计的设计
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 评估包装图像的美学质量是主观的,并且由于复杂的布局构成而复杂.
- 现有的方法在评估视觉设计元素方面缺乏客观性和智力.
- 在包装设计中,需要自动化和可靠的审美质量评估至关重要.
研究的目的:
- 开发一个客观和智能方法来评估包装图像的美学质量.
- 将图像组合特征与图形神经网络 (GNN) 结合起来,以提高设计评估.
- 提高视觉传播设计中的审美质量评估的准确性和一致性.
主要方法:
- 提出了一种包装设计美学质量评估方法,将图像组成特征和图形神经网络 (CGA-GNN) 结合起来.
- 使用图形构造规则 (例如对称性,近距离,三分之一规则) 提取视觉结构信息.
- 集成了一个图表注意力机制,以提高节点特征聚合期间的组成意识.
主要成果:
- 在预测准确性和一致性方面,CGA-GNN显著优于基线模型 (GraphSAGE-GAT,GAT,CNN).
- 达到0.378 ± 0.018的加权根平均平方误差 (WRMSE) 和0.714 ± 0.017.17的斯皮尔曼等级相关系数.
- 通过整合所有三个组成规则来证明最佳性能,验证了多规则集成.
结论:
- 组合规则和GNN的深度整合有效地评估了包装图像的美学质量.
- CGA-GNN为设计评估提供了强大而客观的方法,超越了传统方法.
- 这些发现支持标准化的设计评估,个性化的建议和视觉设计中的创意援助.
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