Categorical Colormap Optimization with Visualization Case Studies
Categorical Colormap Optimization with Visualization Case Studies
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DOI：
10.1109/tvcg.2016.2599214
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发表时间：
2017-01-01
影响因子：
5.2
通讯作者：
Chen, M.
中科院分区：
文献类型：
--
作者：
Fang, H.;Walton, S.;Chen, M.
将一组分类值映射到不同的颜色是数据可视化中的基本技术。可视化软件的用户通常依赖于系统提供的默认色彩映射表，或ColorBrewer等软件建议的色彩映射表。在实践中，用户经常必须以语义上有意义的方式（例如，基于惯例、颜色隐喻和逻辑关联），因此希望确保它们的感知区分得到优化。在本文中，我们提出了一种算法的方法，最大限度地提高一组给定的颜色之间的感知距离。我们解决优化中的两个技术问题，即，(i)局部最大值的现象，停止优化太快，和（ii）的任意重新分配的颜色，导致损失的原始语义关联。我们特别关注用户在优化过程中可能希望施加的不同类型的约束。为了证明这项工作的有效性，我们在两个案例研究中测试了这种技术。为了接触到更广泛的用户，我们还开发了一个名为Colourmap Hospital的网络应用程序。
Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital.