InfoColorizer：信息图表的交互式调色板推荐
InfoColorizer: Interactive Recommendation of Color Palettes for Infographics
摘要：
在设计信息图时，一般用户通常会使用现有的信息图创作工具来获得所需的调色板，这些工具有时会牺牲可定制性，需要设计专业知识，或者忽略元素空间排列的影响。我们提出了一种数据驱动的方法，该方法通过考虑用户的偏好来提供灵活性，通过自动化降低专业知识障碍，并根据元素的空间布局定制建议的调色板。我们通过利用深度学习技术来构建推荐引擎，以从数据中描述良好的色彩设计实践，并进一步开发InfoColorizer，这是一种允许用户以交互和动态方式获取信息图表调色板的工具。为了验证我们的方法，我们进行了全面的四部分评估，包括案例研究，对照用户研究，调查研究和访谈研究。结果表明，InfoColorizer可以提供令人信服的调色板建议，具有足够的灵活性，使用户能够有效地获得高质量的颜色设计输入信息图形与低的努力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
