一种基于卷积神经网络的方法,用于在艺术设计中聚类情感元素
1School of Art, Sichuan University Jinjiang College, Meishan, Sichuan, China.
PeerJ. Computer science
|September 14, 2023
概括
这项研究使用人工智能 (AI) 来分析视觉通信艺术中的情绪. 一个结合卷积神经网络 (CNN) 和变化自编码器 (VAE) 的新框架,有效地根据情感对海报设计进行集群.
科学领域:
- 艺术和设计中的人工智能
- 视觉传播理论 视觉传播理论
- 计算美学 计算美学
背景情况:
- 工业化推动了新的艺术和设计理论,特别是在视觉传播方面.
- 人工智能 (AI) 提供了提取抽象数据和加速艺术设计的工具.
- 了解视觉沟通中的抽象情感方面对于现代设计至关重要.
研究的目的:
- 在视觉传播艺术设计方法学中研究抽象的情感维度.
- 开发和评估基于人工智能的框架,用于海报设计中的情感分析.
- 根据情感内容对视觉艺术进行分类.
主要方法:
- 利用卷积神经网络 (CNN) 来从海报视觉信息中提取特征.
- 采用变异自编码器 (VAE) 来聚合提取的情感特征.
- 将海报图像分为积极,消极和中立情绪类别.
主要成果:
- 实现了超过0.7的轮系数用于图像聚类.
- 在单个情绪类测试中,在80%以上的集群准确性和效率得到了证明.
- 验证了CNN-VAE框架在分析动态设计元素方面的有效性.
结论:
- 拟议的CNN-VAE集群框架有效地分析视觉传播艺术中的情感内容.
- 这种由人工智能驱动的方法为艺术和设计中的情感分析提供了一种新的方法.
- 该框架增强了对情感共视觉设计的理解和创作.
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