Lin, SharonFortuna, JulieKulkarni, ChinmayStone, MaureenHeer, JeffreyB. Preim, P. Rheingans, and H. Theisel2015-02-282015-02-2820131467-8659https://doi.org/10.1111/cgf.12127We introduce an algorithm for automatic selection of semantically-resonant colors to represent data (e.g., using blue for data about ''oceans'', or pink for ''love''). Given a set of categorical values and a target color palette, our algorithm matches each data value with a unique color. Values are mapped to colors by collecting representative images, analyzing image color distributions to determine value-color affinity scores, and choosing an optimal assignment. Our affinity score balances the probability of a color with how well it discriminates among data values. A controlled study shows that expert-chosen semantically-resonant colors improve speed on chart reading tasks compared to a standard palette, and that our algorithm selects colors that lead to similar gains. A second study verifies that our algorithm effectively selects colors across a variety of data categories.H.5.m [Information Interfaces]MiscColor10.1111/cgf.12127