Premium
Selecting Semantically‐Resonant Colors for Data Visualization
Author(s) -
Lin Sharon,
Fortuna Julie,
Kulkarni Chinmay,
Stone Maureen,
Heer Jeffrey
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12127
Subject(s) - palette (painting) , computer science , categorical variable , artificial intelligence , set (abstract data type) , chart , visualization , data set , computer vision , pattern recognition (psychology) , mathematics , statistics , machine learning , programming language , operating system
Abstract We 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.