AI 中文总结
提出Dynamic Tree Colors动态层次颜色图，权衡可区分性与颜色稳定性，经含18名参与者的用户研究验证，其在多数场景表现良好但在Cuttlefish专属场景性能不及后者。
AI 中文摘要
层次颜色图可支持用户分析层次数据。对于大型层次结构，动态颜色图可在用户交互时提升可区分性，但增量颜色变化可能导致用户在数据集中迷失方向。为解决这一挑战，我们提出Dynamic Tree Colors，一种可配置于可区分性与颜色稳定性间合适权衡的动态层次颜色图。我们还为这两个标准定义了质量指标，并针对这些指标及包含18名参与者的用户研究，研究了我们算法的性能。结果表明，Dynamic Tree Colors在广泛应用场景中表现良好，但在算法Cuttlefish专为设计的特定场景中，其性能未达到该最先进算法的水平。
英文摘要
Hierarchical color maps can support users in the analysis of hierarchical data. For large hierarchies, dynamic color maps can improve discriminability upon user interactions, but the incremental color changes may cause users to lose their orientation in the data set. To address this challenge, we present Dynamic Tree Colors, a dynamic hierarchical color map that can be configured to a suitable tradeoff between discriminability and color stability. We also define quality metrics for both criteria and investigate our algorithm's performance with respect to these metrics as well as a user study with 18 participants. Our results indicate that Dynamic Tree Colors yields good results in a wide range of application scenarios, but it does not achieve the performance of the state-of-the-art algorithm Cuttlefish in the specific scenario that algorithm was designed for.
Comments11 pages, 12 figures, supplemental materials available at: https://osf.io/xfp8n/
Journal refMertz, T., Reynolds, S. L. and Kohlhammer, J. (2026). In Proceedings of the 21st International Conference on Computer Graphics, Interaction and Visualization Theory and Applications - GRIVAPP; SciTePress, pages 27-38
