VividGraph: Learning to Extract and Redesign Network Graphs from Visualization Images

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Sicheng Song, Chenhui Li, Yujing Sun, Changbo Wang

 View presentation:Thursday, October 20th, 2022 @ 16:45GMT+00:00Change your timezone on the schedule page
2 years agoYour current time: Monday, Apr 7th @ 22:59

Exemplar figure, described by caption below — VividGraph can be used in many practical applications. The input is a bitmap. Through our semantic segmentation and connection algorithm, we can obtain its underlying data. Using the extracted data, we can reconstruct the vector of the graph and redesign the chart, such as recoloring, re-layout, and data modification.

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Keywords

Information visualization , Network graph , Data extraction , Chart recognition , Semantic segmentation , Redesign

Abstract

Network graphs are common visualization charts. They often appear in the form of bitmaps in papers, web pages, magazine prints, and designer sketches. People often want to modify network graphs because of their poor design, but it is difficult to obtain their underlying data. In this paper, we present VividGraph, a pipeline for automatically extracting and redesigning network graphs from static images. We propose using convolutional neural networks to solve the problem of network graph data extraction. Our method is robust to hand-drawn graphs, blurred graph images, and large graph images. We also present a network graph classification module to make it effective for directed graphs. We propose two evaluation methods to demonstrate the effectiveness of our approach. It can be used to quickly transform designer sketches, extract underlying data from existing network graphs, and interactively redesign poorly designed network graphs.