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Color Names Across Languages: Salient Colors and Term Translation in Multilingual Color Naming Models

Younghoon Kim, Kyle Thayer, Gabriella Silva Gorsky, Jeffrey Heer. Computer Graphics Forum (Proc. EuroVis), 2019
Figure for Color Names Across Languages: Salient Colors and Term Translation in Multilingual Color Naming Models
Maximum probability maps of English and Korean color terms. Each point represents a 10 x 10 x 10 bin in CIELAB color space. Larger points have a greater likelihood of agreement on a single term. Each bin is colored using the average color of the most probable name term. English has 10 clusters corresponding to basic English color terms, whereas Korean exhibits additional clusters.
Materials
Abstract
Color names facilitate the identification and communication of colors, but may vary across languages. We contribute a set of human color name judgments across 14 common written languages and build probabilistic models that find different sets of nameable (salient) colors across languages. For example, we observe that unlike English and Chinese, Russian and Korean have more than one nameable blue color among fully-saturated RGB colors. In addition, we extend these probabilistic models to translate color terms from one language to another via a shared perceptual color space. We compare Korean-English trans- lations from our model to those from online translation tools and find that our method better preserves perceptual similarity of the colors corresponding to the source and target terms. We conclude with implications for visualization and future research.
BibTeX
@article{2019-multi-lingual-color-names,
  title = {Color Names Across Languages: Salient Colors and Term Translation in Multilingual Color Naming Models},
  author = {Kim, Younghoon AND Thayer, Kyle AND Gorsky, Gabriella AND Heer, Jeffrey},
  journal = {Computer Graphics Forum (Proc. EuroVis)},
  year = {2019},
  url = {https://idl.uw.edu/papers/multi-lingual-color-names},
  doi = {10.2312/evs.20191166}
}