Pay "Attention" to Chart Images for What You Read on Text

Chenyu Yang, Ruixue Fan, Nan Tang, Meihui Zhang, Xiaoman Zhao, Ju Fan, Xiaoyong Du
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Abstract

Data visualization is changing how we understand data, by showing why's, how's, and what's behind important patterns/trends in almost every corner of the world, such as in academic papers, news articles, financial reports, etc. However, along with the increasing complexity and richness of data visualizations, given a text description (e.g., "fewer teens say they attended school completely online (8%)"), it becomes harder for users to pinpoint where to pay attention to on a chart (e.g., a grouped bar chart). In this demonstration paper, we present a system HiChart for text-chart image highlighting: when a user selects a span of text, HiChart automatically analyzes the chart image (e.g., a jpeg or a png file) and highlights the parts that are relevant to the span. From a technical perspective, HiChart devises the following techniques. Reverse-engineering visualizations: given a chart image, HiChart uses computer vision techniques to generate a visualization specification using Vega-Lite language, as well as the underlying dataset; Visualization calibration by data tuning: HiChart calibrates the re-generated chart by tuning the recovered dataset through value perturbation; and Chart highlighting for a span: HiChart maps the span to corresponding data cells and uses the built-in highlighting functions of Vega-Lite to highlight the chart.
要“注意”在阅读文字时使用图表图片
数据可视化正在改变我们理解数据的方式,通过展示世界上几乎每个角落的重要模式/趋势背后的原因、方法和内容,例如学术论文、新闻文章、财务报告等。然而,随着数据可视化的复杂性和丰富性的增加,给定一个文本描述(例如,“更少的青少年说他们完全在线上学(8%)”),用户很难确定图表(例如,分组条形图)上需要注意的地方。在这篇演示论文中,我们展示了一个用于文本图表图像高亮显示的系统HiChart:当用户选择一个文本跨度时,HiChart会自动分析图表图像(例如jpeg或png文件),并高亮显示与该跨度相关的部分。从技术角度来看,HiChart设计了以下技术。逆向工程可视化:给定图表图像,HiChart使用计算机视觉技术使用Vega-Lite语言生成可视化规范,以及底层数据集;通过数据调优的可视化校准:HiChart通过值扰动对恢复的数据集进行调优来校准重新生成的图表;对跨度进行图表高亮显示:HiChart将跨度映射到相应的数据单元格,并使用Vega-Lite内置的高亮显示功能来高亮显示图表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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