PaCoNet：用于平行坐标的深度数据提取

中文摘要 AI 辅助

从可视化图表中提取数据长期以来一直是计算机视觉领域的挑战，当前研究主要聚焦于条形图、折线图、饼图等低维可视化形式。然而，作为一种广泛使用的高维数据可视化方法，平行坐标在该领域的研究仍基本处于未开发状态。由于平行坐标图若设计不当或数据密集时会迅速变得杂乱且难以解读，因此从这类可视化图表中自动提取数据具有特殊的研究意义。本文中，我们提出了PaCoNet，这是首个用于平行坐标数据提取的方法。PaCoNet不仅能够提取线条坐标，还能实现对单个数据样本的提取以用于进一步分析。为此，我们做出了以下贡献：我们提出了首个针对平行坐标分析量身定制的深度学习方法，并证明其性能显著优于未适配的基线方法；我们还引入了一个用于训练和测试的大规模平行坐标数据集。这些关键贡献首次实现了平行坐标图的自动分析与重新设计，PaCoNet因此为复杂可视化分析奠定了基础，并进一步推动了计算机视觉与数据可视化的交叉融合。所有代码、训练好的模型及数据生成脚本将在论文接收后公开。

英文摘要

Extracting data from visualizations has long challenged computer vision, with current research focused on bar, line, and pie charts, among other low-dimensional visualizations. However, parallel coordinates as a widely used high-dimensional data visualization approach, remain largely unexplored in this context. As parallel coordinate plots can quickly become cluttered and difficult to interpret when poorly designed or densely populated, automated data extraction from such visualizations is of particular interest. In this paper, we propose PaCoNet, the first approach for parallel coordinate data extraction. PaCoNet not only extracts line coordinates, but also enables the extraction of individual data samples for further analysis. Towards this end, we make the following contributions. We present the first deep learning approach tailored for parallel coordinate analysis, and demonstrate that it outperforms unadapted baselines by a significant margin. We further introduce a large-scale parallel coordinate dataset for training and testing. Together, these key contributions enable for the first time the automated analysis and redesign of parallel coordinate plots. PaCoNet thus lays the groundwork for complex visualization analysis, and further advances the intersection of computer vision and data visualization. All code, trained models, and data generation scripts will be made publicly available upon acceptance of the paper.

发表机构

• Ulm University（乌尔姆大学）
• Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Accepted at ICPR 2026