基于深度学习的可视化图表分类方法研究  

Research on visualization chart classification method based on deep learning

作　　者：张明凯 胡军国[1] 刘江南 邓飞[1] 尹文杰 Zhang Mingkai;Hu Junguo;Liu Jiangnan;Deng Fei;Yin Wenjie(College of Mathematics and Computer Science,Zhejiang A&F University,Hangzhou 311300,China;College of Chemistry and Materials Engineering,Zhejiang A&F University,Hangzhou 311300,China)

机构地区：[1]浙江农林大学数学与计算机科学学院,浙江杭州311300 [2]浙江农林大学化学与材料工程学院,浙江杭州311300

出　　处：《电子技术应用》2024年第5期58-65,共8页Application of Electronic Technique

摘　　要：可视化图表的分类研究对于图表理解和文档解析具有很大的意义。分别通过爬虫和软件生成的方式,构建了两个包含16类常见图表的数据集,该数据集在数量、类型和样式丰富性上具有一定的优势。在3个数据集上实验对比了Transformer架构和卷积神经网络架构的模型,结果表明Transformer架构在图表分类任务上具有一定优势。基于Swin Transformer模型,设计了多种数据增强策略,在增加模型泛化性的同时也引入了分布差异;通过对不同策略训练出的模型预测进行均值融合,同单模型相比分类性能有较大提升。在6个测试集上对集成模型进行了测试,分类准确率均大于0.9;对于图像质量高、视觉形式简单的生成图表,模型分类准确率接近1。The classification research of visual charts holds significant implications for chart comprehension and document parsing.This paper has constructed two datasets,each containing 16 common chart types,using web scraping and software generation.These datasets exhibit certain advantages in terms of quantity,type,and stylistic diversity.This paper has also conducted experiments comparing Transformer and Convolutional Neural Network(CNN)architectures on three datasets,and the results indicates that the Transformer architecture has certain advantages in the task of chart classification.Utilizing the Swin Transformer model,this paper designs various data augmentation strategies,not only increasing the generalization of the model,but also introducing the distribution difference.By averaging predictions from models trained with different strategies,there is a significant improvement in classification performance compared to individual models.The ensemble model was tested on 6 test sets,with classification accuracy exceeding 0.9 in all cases.For generated charts with high image quality and simple visual forms,the model's classification accuracy approached 1.

关 键 词：图表分类 图表理解 卷积神经网络 Swin Transformer 模型集成 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]