We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-ofthe-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.

中文翻译：

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Palettailor：分类数据的可区分着色

我们提出了一种为不同的可视化（例如多类散点图、折线图和条形图）创建和分配调色板的集成方法。虽然其他方法将颜色的创建与其分配分开，但我们的方法会考虑数据特征来生成调色板，然后以促进更好的视觉区分的方式分配调色板。为此，我们使用基于模拟退火的定制优化来最大化三个精心设计的颜色评分函数的组合：点差异、名称差异和颜色辨别。我们将我们对最先进调色板的方法与散点图和折线图的受控用户研究进行比较，此外我们还进行了案例研究。我们的结果表明，Palettailor 作为一种全自动方法，生成具有比现有方法更高辨别质量的调色板。我们优化的效率还使我们能够将用户修改纳入颜色选择过程。