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Interactive Context-Preserving Color Highlighting for Multiclass Scaterplots
被引:0
|作者:
Lu, Kecheng
[1
]
Reda, Khairi
[2
]
Deussen, Oliver
[3
]
Wang, Yunhai
[1
]
机构:
[1] Shandong Univ, Jinan, Shandong, Peoples R China
[2] Indiana Univ Purdue Univ, Indianapolis, IN USA
[3] Univ Konstanz, Constance, Germany
来源:
PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023
|
2023年
关键词:
Color Palettes;
Highlighting;
Multi-Class Scatterplots;
Discriminability;
OPTIMIZATION;
DIFFERENCE;
MODEL;
D O I:
10.1145/3544548.3580734
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
Color is one of the main visual channels used for highlighting elements of interest in visualization. However, in multi-class scatterplots, color highlighting often comes at the expense of degraded color discriminability. In this paper, we argue for context-preserving highlighting during the interactive exploration of multi-class scatterplots to achieve desired pop-out effects, while maintaining good perceptual separability among all classes and consistent color mapping schemes under varying points of interest. We do this by first generating two contrastive color mapping schemes with large and small contrasts to the background. Both schemes maintain good perceptual separability among all classes and ensure that when colors from the two palettes are assigned to the same class, they have a high color consistency in color names. We then interactively combine these two schemes to create a dynamic color mapping for highlighting different points of interest. We demonstrate the effectiveness through crowd-sourced experiments and case studies.
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页数:15
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