Interactive Context-Preserving Color Highlighting for Multiclass Scatterplots

要旨

Color is one of the main visual channels used for highlighting elements of interest in visualization. However, in multi-class scatterplots, color highlighting often comes at the expense of degraded color discriminability. In this paper, we argue for context-preserving highlighting during the interactive exploration of multi-class scatterplots to achieve desired pop-out effects, while maintaining good perceptual separability among all classes and consistent color mapping schemes under varying points of interest. We do this by first generating two contrastive color mapping schemes with large and small contrasts to the background. Both schemes maintain good perceptual separability among all classes and ensure that when colors from the two palettes are assigned to the same class, they have a high color consistency in color names. We then interactively combine these two schemes to create a dynamic color mapping for highlighting different points of interest. We demonstrate the effectiveness through crowd-sourced experiments and case studies.

著者
Kecheng Lu
Shandong University, Qingdao, Shandong, China
Khairi Reda
Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States
Oliver Deussen
University of Konstanz, Konstanz, Germany
Yunhai Wang
Shandong University, Qingdao, China
論文URL

https://doi.org/10.1145/3544548.3580734

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Visualization Perception

Hall F
6 件の発表
2023-04-24 23:30:00
2023-04-25 00:55:00