  When designing infographics, general users usually struggle with getting
desired color palettes using existing infographic authoring tools, which
sometimes sacrifice customizability, require design expertise, or neglect the
influence of elements' spatial arrangement. We propose a data-driven method
that provides flexibility by considering users' preferences, lowers the
expertise barrier via automation, and tailors suggested palettes to the spatial
layout of elements. We build a recommendation engine by utilizing deep learning
techniques to characterize good color design practices from data, and further
develop InfoColorizer, a tool that allows users to obtain color palettes for
their infographics in an interactive and dynamic manner. To validate our
method, we conducted a comprehensive four-part evaluation, including case
studies, a controlled user study, a survey study, and an interview study. The
results indicate that InfoColorizer can provide compelling palette
recommendations with adequate flexibility, allowing users to effectively obtain
high-quality color design for input infographics with low effort.
