不同图表类型的合成数据集,用于图表识别和可视化方面的进步
Filip Bajić1, Marija Habijan2, Krešimir Nenadić2
1University Computing Centre, University of Zagreb, 10000 Zagreb, Croatia.
Data in brief
|March 4, 2024
概括
一个新的合成图表数据集有助于在数据可视化和解释方面开发算法. 本资源支持训练深度模型,以便在各种类型和分辨率上增强图表分类和理解.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 数据可视化方面的进步需要强大的数据集来训练算法.
- 当前的数据集可能缺乏图表类型的多样性或用于基准测试的标准化.
- 自动提取方法对于高效的数据集创建至关重要.
研究的目的:
- 引入一个合成图表数据集,用于推进数据可视化算法.
- 为培训和测试深度学习模型提供标准化资源.
- 建立一个基准来评估图表解释方面的表现.
主要方法:
- 完全自动的低级算法用于图形元素提取.
- 数据集包括各种图表类型 (区域,条形,盒子,甜甜圈,线条,饼,散点).
- 图像仅限于2D表示,单个图表和特定分辨率以提高效率.
主要成果:
- 一个精心策划的合成图表数据集现在可用于研究.
- 数据集按分辨率和图表类型分为训练和测试子集.
- 数据集促进了数据理解算法的直接比较和评估.
结论:
- 合成图表数据集显著支持可视化分类和解释的算法进步.
- 研究人员可以利用这个数据集来提高深度模型适应性和系统性能.
- 这个资源促进了自动化数据理解和可视化分解的进步.
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