Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots
Publication in refereed journal

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摘要Appropriate choice of colors significantly aids viewers in understanding the structures in multiclass scatterplots and becomes more important with a growing number of data points and groups. An appropriate color mapping is also an important parameter for the creation of an aesthetically pleasing scatterplot. Currently, users of visualization software routinely rely on color mappings that have been pre-defined by the software. A default color mapping, however, cannot ensure an optimal perceptual separability between groups, and sometimes may even lead to a misinterpretation of the data. In this paper, we present an effective approach for color assignment based on a set of given colors that is designed to optimize the perception of scatterplots. Our approach takes into account the spatial relationships, density, degree of overlap between point clusters, and also the background color. For this purpose, we use a genetic algorithm that is able to efficiently find good color assignments. We implemented an interactive color assignment system with three extensions of the basic method that incorporates top K suggestions, user-defined color subsets, and classes of interest for the optimization. To demonstrate the effectiveness of our assignment technique, we conducted a numerical study and a controlled user study to compare our approach with default color assignments; our findings were verified by two expert studies. The results show that our approach is able to support users in distinguishing cluster numbers faster and more precisely than default assignment methods.
著者Yunhai Wang, Xin Chen, Tong Ge, Chen Bao, Michael Sedlmair, Chi-Wing Fu, Deussen Oliver, Chen Baoquan
期刊名稱IEEE Transactions on Visualization and Computer Graphics
詳細描述full paper in IEEE Visualization week 2018 (InfoVis track)
出版年份2019
月份1
卷號25
期次1
出版社IEEE
頁次820 - 829
國際標準期刊號1077-2626
電子國際標準期刊號1941-0506
語言美式英語

上次更新時間 2019-12-12 於 03:39