Palettailor: Discriminable Colorization for Categorical Data

K. Lu, M. Feng, X. Chen, M. Sedlmair, O. Deussen, D. Lischinski, Z. Cheng, Y. Wang

Material

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{Lu2020PalettailorDiscriminableColorization,
  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-ofthe-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.},
  archiveprefix = {arXiv},
  author     = {K. Lu and M. Feng and X. Chen and M. Sedlmair and O. Deussen and D. Lischinski and Z. Cheng and Y. Wang},
  eprint     = {2009.02969},
  file       = {:http\://arxiv.org/pdf/2009.02969v1:PDF},
  keywords   = {cs.GR},
  month      = {sep},
  primaryclass = {cs.GR},
  title      = {Palettailor: Discriminable Colorization for Categorical Data},
  year       = {2020},
  url        = {http://graphics.uni-konstanz.de/publikationen/Lu2020PalettailorDiscriminableColorization},
}