Back to Search Start Over

Palettailor: Discriminable Colorization for Categorical Data.

Authors :
Lu, Kecheng
Feng, Mi
Chen, Xin
Sedlmair, Michael
Deussen, Oliver
Lischinski, Dani
Cheng, Zhanglin
Wang, Yunhai
Source :
IEEE Transactions on Visualization & Computer Graphics; Feb2021, Vol. 27 Issue 2, p475-484, 10p
Publication Year :
2021

Abstract

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10772626
Volume :
27
Issue :
2
Database :
Complementary Index
Journal :
IEEE Transactions on Visualization & Computer Graphics
Publication Type :
Academic Journal
Accession number :
148496999
Full Text :
https://doi.org/10.1109/TVCG.2020.3030406