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arXiv · 2102.02041

InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

Lin-Ping Yuan·Ziqi Zhou·Jian Zhao·Yiqiu Guo·Fan Du·Huamin Qu
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Abstract

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.

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Lin-Ping Yuan, Ziqi Zhou, Jian Zhao, Yiqiu Guo, Fan Du, Huamin Qu. 2021-02-03. InfoColorizer: Interactive Recommendation of Color Palettes for Infographics. https://arxiv.org/abs/2102.02041

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