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.
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.