在视觉通信设计中的视觉上下文感知和用户情感反
1Academy of Arts, Qujing Normal University, Qujing, Yunnan, 655011, China. zhujiayi@mail.qjnu.edu.cn.
BMC psychology
|March 29, 2025
概括
本研究介绍了DA-MLCNN,这是一种用于视觉情绪分析的新方法,它使用双重注意力机制结合了整体和局部图像特征. 这种方法显著提高了情绪分类的准确性,推进了视觉通信设计.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 视觉通信设计在网络应用中至关重要.
- 目前的情绪分析方法忽视了局部情绪线索和多种道特征.
- 需要先进的视觉情感分析技术.
研究的目的:
- 开发一种双重注意的多层特征融合方法 (DA-MLCNN) 进行增强的视觉情感分析.
- 解决现有方法在图像中捕捉微妙的情感表达的局限性.
- 改进视觉特征的语义挖掘,以准确地分类情感.
主要方法:
- 多层卷积神经网络 (CNN) 可以提取高层和低层图像特征.
- 空间注意力机制增强了低层特征,而通道注意力机制增强了高层特征.
- 功能被融合和协调,以创建含义丰富的视觉表示,用于情感分类.
主要成果:
- 在Twitter 2017数据集上达到79.8%的准确率,在情绪ROI数据集上达到55.8%.
- 在特定情绪方面取得了很高的准确性:在情绪ROI数据集中,悲伤为89%,惊喜为94%,快乐为91%.
- 在二分法和多类别情绪数据集上展示了改进的分类性能.
结论:
- 拟议的DA-MLCNN方法有效地提取有区别的视觉特征,以进行优越的情绪分析.
- 该方法增强了视觉情感分析,为视觉通信设计提供了新的可能性.
- 这些发现强调了局部特征和注意力机制在理解基于图像的情绪方面的重要性.
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