Infographics are engaging visual representations that tell an informative story using a fusion of data and graphical elements. The large variety of infographic design poses a challenge for their high-level analysis. We use the concept of Visual Information Flow (VIF), which is the underlying semantic structure that links graphical elements to convey the information and story to the user. To explore VIF, we collected a repository of over 13K infographics. We use a deep neural network to identify visual elements related to information, agnostic to their various artistic appearances. We construct the VIF by automatically chaining these visual elements together based on Gestalt principles. Using this analysis, we characterize the VIF design space by a taxonomy of 12 different design patterns. Exploring in a real-world infographic dataset, we discuss the design space and potentials of VIF in light of this taxonomy.
|Title of host publication||CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems|
|Publisher||Association for Computing Machinery|
|State||Published - 21 Apr 2020|
|Event||2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020 - Honolulu, United States|
Duration: 25 Apr 2020 → 30 Apr 2020
|Name||Conference on Human Factors in Computing Systems - Proceedings|
|Conference||2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020|
|Period||25/04/20 → 30/04/20|
Bibliographical noteFunding Information:
We thank the reviewers for their valuable comments and Freepik, Shutterstock for high-quality infographic designs. This work is supported in parts by NSFC (61761146002, 61602310, 61802265), Guangdong Provincial Natural Science Foundation (2018A030310426), Shenzhen Innovation Program (JCYJ20170302154106666), LHTD (20170003), the National Engineering Laboratory for Big Data System Computing Technology, and the Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ).
© 2020 ACM.
- design analysis
- visual information flow
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Human-Computer Interaction