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Dynamic Color Assignment for Hierarchical Data

Jiashu Chen, Weikai Yang, Zelin Jia, Lanxi Xiao, Shixia Liu

TL;DR

This work develops a dynamic color assignment method for hierarchical data that simultaneously considers color discriminability, color harmony, and spatial distribution at each hierarchical level and uses the colors of parent classes to guide the color assignment of their child classes.

Abstract

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.

Dynamic Color Assignment for Hierarchical Data

TL;DR

This work develops a dynamic color assignment method for hierarchical data that simultaneously considers color discriminability, color harmony, and spatial distribution at each hierarchical level and uses the colors of parent classes to guide the color assignment of their child classes.

Abstract

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.
Paper Structure (20 sections, 7 equations, 11 figures, 2 tables)

This paper contains 20 sections, 7 equations, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Requirement Analysis
  4. Dynamic Color Assignment
  5. Method Overview
  6. Color Assignment
  7. Color Discriminability
  8. Color Harmony
  9. Spatial Distribution
  10. Optimization
  11. Color Range Selection
  12. Default Color Range
  13. Dynamic Color Range
  14. Evaluation
  15. Quantitative Evaluation
  16. ...and 5 more sections

Figures (11)

  • Figure 1: Results generated by existing color assignment methods.
  • Figure 2: Method overview. Given data, the color range selection module selects an appropriate range of colors. Then, the color assignment module generates a discriminable and harmonic color assignment result within that range.
  • Figure 3: Color harmony: (a) on the hue wheel, colors that lie in the gray region are considered harmonic; (b) on the chroma-lightness plane, colors that follow a straight line are considered harmonic.
  • Figure 4: The continuation method improves assignment result by sequentially incorporating discriminability, harmony, and spatial distribution.
  • Figure 5: The workflow of dynamic color range selection.
  • ...and 6 more figures