Visual Computing

University of Konstanz
IEEE Transactions on Visualization and Computer Graphics

Palettailor: Discriminable Colorization for Categorical Data

K. Lu, M. Feng, X. Chen, M. Sedlmair, O. Deussen, D. Lischinski, Z. Cheng, Y. Wang
Teaser of Palettailor: Discriminable Colorization for Categorical Data

Material

Paper (.pdf, 8.2 MB)

Abstract

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-classscatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes datacharacteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination ofclasses. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefullydesigned 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 showthat 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

BibTeX

@Article{Lu2021PalettailorDiscriminableColorization,
  author    = {Kecheng Lu and Mi Feng and Xin Chen and Michael Sedlmair and Oliver Deussen and Dani Lischinski and Zhanglin Cheng and Yunhai Wang},
  journal   = {IEEE Transactions on Visualization and Computer Graphics},
  title     = {Palettailor: Discriminable Colorization for Categorical Data},
  year      = {2021},
  month     = {feb},
  number    = {2},
  pages     = {475--484},
  volume    = {27},
  doi       = {10.1109/tvcg.2020.3030406},
  publisher = {{IEEE}},
}