Palettailor: Discriminable colorization for categorical data

Kecheng Lu, Mi Feng, Xin Chen, Michael Sedlmair, Oliver Deussen, Dani Lischinski, Zhanglin Cheng*, Yunhai Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageAmerican English
Pages (from-to)475-484
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number2
StatePublished - Feb 2021

Bibliographical note

Publisher Copyright:
© 1995-2012 IEEE.


  • Bar Chart
  • Color Palette
  • Discriminability
  • Line Chart
  • Multi-Class Scatterplot


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