Assigning discriminable and harmonic colors to samples according to their class labels and spatial distribution can generate attractive visualizations and facilitate data exploration. However, as the number of classes increases, it is challenging to generate a high-quality color assignment result that accommodates all classes simultaneously. A practical solution is to organize classes into a hierarchy and then dynamically assign colors during exploration. However, existing color assignment methods fall short in generating high-quality color assignment results and dynamically aligning them with hierarchical structures. To address this issue, we develop a dynamic color assignment method for hierarchical data, which is formulated as a multi-objective optimization problem. This method simultaneously considers color discriminability, color harmony, and spatial distribution at each hierarchical level. By using the colors of parent classes to guide the color assignment of their child classes, our method further promotes both consistency and clarity across hierarchical levels. We demonstrate the effectiveness of our method in generating dynamic color assignment results with quantitative experiments and a user study.

翻译：根据样本的类别标签和空间分布为其分配可区分且和谐的色彩，能够生成具有吸引力的可视化效果并促进数据探索。然而，随着类别数量的增加，要同时为所有类别生成高质量的色彩分配结果具有挑战性。一种实用的解决方案是将类别组织成层次结构，然后在探索过程中动态分配色彩。然而，现有的色彩分配方法在生成高质量的分配结果并使其与层次结构动态对齐方面存在不足。为解决这一问题，我们提出了一种面向层次化数据的动态色彩分配方法，该方法被构建为一个多目标优化问题。该方法在每一层次上同时考虑色彩可区分性、色彩和谐度以及空间分布。通过使用父类别的色彩来指导其子类别的色彩分配，我们的方法进一步提升了跨层次的一致性与清晰度。我们通过定量实验和用户研究验证了本方法在生成动态色彩分配结果方面的有效性。