Chenyu Yang, Ruixue Fan, Nan Tang, Meihui Zhang, Xiaoman Zhao, Ju Fan, Xiaoyong Du
{"title":"Pay \"Attention\" to Chart Images for What You Read on Text","authors":"Chenyu Yang, Ruixue Fan, Nan Tang, Meihui Zhang, Xiaoman Zhao, Ju Fan, Xiaoyong Du","doi":"10.1145/3555041.3589714","DOIUrl":null,"url":null,"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.","PeriodicalId":161812,"journal":{"name":"Companion of the 2023 International Conference on Management of Data","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion of the 2023 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555041.3589714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.