数据驱动的文化背景融合用于环境艺术图像分类:双核挤压和激发网络的技术支持
1Shaanxi Fashion Engineering University, Xi'an City, China.
PloS one
|March 20, 2025
概括
一个新的双核挤压和刺激网络 (DKSE-Net) 通过融合文化背景来改善环境艺术图像的分类. 这种深度学习模型实现了92.7%的准确性,超过了现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数字艺术 数字艺术 数字艺术
背景情况:
- 环境艺术图像呈现复杂的文化背景和多样化的视觉特征.
- 由于文化细微差别和视觉变异性,对这些图像的准确分类具有挑战性.
- 现有的方法往往难以有效地将文化背景纳入图像分类中.
研究的目的:
- 开发一种以数据为导向的方法,用于环境艺术图像分类中的文化背景融合.
- 提出一种新的深度学习模型,即双核挤压和激发网络 (DKSE-Net),用于增强功能提取.
- 通过整合文化信息来提高环境艺术图像分类的准确性和稳定性.
主要方法:
- 提出了一个新的双核挤压和激发网络 (DKSE-Net) 模型.
- 集成选择性内核网络 (SKNet) 用于适应性受体场调整和挤压和激发网络 (SENet) 用于道特征增强.
- 采用了包括扩展卷积,L2规范化,脱落,整正线性单元激活,深度卷积和批量正常化在内的技术.
主要成果:
- 该DKSE-Net模型的分类准确度为92.7%,超过了传统的卷积神经网络 (CNN) 和最先进的模型3.5个百分点.
- 在对具有复杂文化背景的环境艺术图像进行分类方面表现出卓越的表现.
- 展示了多样化的文化特征的有效整合,从而提高了分类准确性和稳定性.
结论:
- 拟议的DKSE-Net模型通过文化背景融合在环境艺术图像分类方面取得了重大进展.
- 该模型能够全面提取全球和本地特征的能力提高了分类性能.
- 这项研究为文化背景意识的图像分类提供了宝贵的参考资料,并突出了环境艺术领域深度学习的潜力.
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