Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea.
IEEE Trans Vis Comput Graph. 2013 Oct;19(10):1746-57. doi: 10.1109/TVCG.2012.315.
Visualization techniques often use color to present categorical differences to a user. When selecting a color palette, the perceptual qualities of color need careful consideration. Large coherent groups visually suppress smaller groups and are often visually dominant in images. This paper introduces the concept of class visibility used to quantitatively measure the utility of a color palette to present coherent categorical structure to the user. We present a color optimization algorithm based on our class visibility metric to make categorical differences clearly visible to the user. We performed two user experiments on user preference and visual search to validate our visibility measure over a range of color palettes. The results indicate that visibility is a robust measure, and our color optimization can increase the effectiveness of categorical data visualizations.
可视化技术通常使用颜色向用户呈现分类差异。在选择调色板时，需要仔细考虑颜色的感知质量。大的连贯组体会对较小的组产生视觉抑制作用，并且在图像中通常具有视觉优势。本文引入了类可视性的概念，用于定量衡量调色板呈现连贯分类结构给用户的效用。我们提出了一种基于我们的类可视性度量的颜色优化算法，以使分类差异对用户清晰可见。我们在用户偏好和视觉搜索方面进行了两项用户实验，以验证我们的可视性度量在一系列调色板中的有效性。结果表明，可视性是一种稳健的度量，我们的颜色优化可以提高分类数据可视化的效果。
