Chen Jiashu, Yang Weikai, Jia Zelin, Xiao Lanxi, Liu Shixia
IEEE Trans Vis Comput Graph. 2025 Jan;31(1):338-348. doi: 10.1109/TVCG.2024.3456386. Epub 2024 Nov 25.
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.
根据样本的类别标签和空间分布为其分配可区分且协调的颜色，可以生成吸引人的可视化效果并便于数据探索。然而，随着类别的数量增加，要同时生成适应所有类别的高质量颜色分配结果具有挑战性。一个实际的解决方案是将类别组织成一个层次结构，然后在探索过程中动态分配颜色。然而，现有的颜色分配方法在生成高质量颜色分配结果以及将它们与层次结构动态对齐方面存在不足。为了解决这个问题，我们开发了一种用于层次数据的动态颜色分配方法，该方法被公式化为一个多目标优化问题。此方法在每个层次级别同时考虑颜色可区分性、颜色协调性和空间分布。通过使用父类的颜色来指导其子类的颜色分配，我们的方法进一步促进了跨层次级别的一致性和清晰度。我们通过定量实验和用户研究证明了我们的方法在生成动态颜色分配结果方面的有效性。
