使用卷积变压器进行美术识别
Yu Liu1, Haozhe Bai1, Jingchao Wang1
1School of Arts, Chongqing University, Chongqing, China.
PeerJ. Computer science
|December 9, 2024
概括
这项研究引入了一种改进的卷积变压器系统,用于识别美术绘画,增强艺术安全. 新模型显著优于现有方法,证明了卷积变压器在图像分类中的有效性.
科学领域:
- 数字图像处理是数字图像处理.
- 计算机视觉 计算机视觉 计算机视觉
- 深度学习是一种深度学习.
背景情况:
- 数字化提高了艺术品的可访问性和保存性,但也带来了盗窃等安全风险.
- 精确识别美术绘画对于提高艺术安全至关重要.
- 当前的艺术识别系统有局限性,需要提高效率.
研究的目的:
- 开发一种改进的识别系统,用于对美术绘画进行分类.
- 通过先进的深度学习技术提高艺术品识别的效率.
- 利用卷积变压器与专注学习的注意力机制.
主要方法:
- 开发了一种新的识别系统,使用具有注意力机制的卷积变压器.
- 集成变压器,一种深度学习架构,以多头注意力而闻名,以提高学习效率.
- 使用转移学习,将拟议的系统与四个预先训练的网络 (ResNet50,VGG16,AlexNet,ViT) 进行了比较.
主要成果:
- 拟议的卷积变压器系统的性能优于现有的模型,包括基于ResNet50,VGG16,AlexNet和ViT的模型.
- 实验结果表明,在美术绘画的分类方面,其表现优越.
- 凸显了卷积变压器对学习复杂图像特征的有效性.
结论:
- 开发的卷积变压器系统为美术绘画识别提供了显著的改进.
- 这种方法提高了保护有价值的艺术品免受盗窃的可能性.
- 卷积变压器在像艺术分析这样的专业领域对图像特征学习非常有效.
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