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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Visualization and Computer Graphics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
