IEEE Trans Vis Comput Graph. 2021 Feb;27(2):337-346. doi: 10.1109/TVCG.2020.3030351. Epub 2021 Jan 28.
In practice, charts are widely stored as bitmap images. Although easily consumed by humans, they are not convenient for other uses. For example, changing the chart style or type or a data value in a chart image practically requires creating a completely new chart, which is often a time-consuming and error-prone process. To assist these tasks, many approaches have been proposed to automatically extract information from chart images with computer vision and machine learning techniques. Although they have achieved promising preliminary results, there are still a lot of challenges to overcome in terms of robustness and accuracy. In this paper, we propose a novel alternative approach called Chartem to address this issue directly from the root. Specifically, we design a data-embedding schema to encode a significant amount of information into the background of a chart image without interfering human perception of the chart. The embedded information, when extracted from the image, can enable a variety of visualization applications to reuse or repurpose chart images. To evaluate the effectiveness of Chartem, we conduct a user study and performance experiments on Chartem embedding and extraction algorithms. We further present several prototype applications to demonstrate the utility of Chartem.
在实践中，图表通常以位图图像的形式存储。虽然它们易于被人类理解，但对于其他用途却不太方便。例如，要更改图表样式、类型或数据值，实际上需要创建一个全新的图表，这通常是一个耗时且容易出错的过程。为了辅助这些任务，许多方法已经被提出，这些方法利用计算机视觉和机器学习技术从图表图像中自动提取信息。尽管它们在初步结果方面取得了有前景的成果，但在鲁棒性和准确性方面仍有许多挑战需要克服。在本文中，我们提出了一种名为 Chartem 的新方法，旨在从根本上直接解决这个问题。具体来说，我们设计了一种数据嵌入方案，将大量信息编码到图表图像的背景中，而不会干扰人类对图表的感知。从图像中提取出的嵌入信息，可以使各种可视化应用程序重复使用或重新利用图表图像。为了评估 Chartem 的有效性，我们对 Chartem 的嵌入和提取算法进行了用户研究和性能实验。我们进一步展示了几个原型应用程序，以演示 Chartem 的实用性。
