期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 27, 期 2, 页码 475-484出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2020.3030406
关键词
Image color analysis; Data visualization; Optimization; Task analysis; Bars; Visualization; Tools; Color Palette; Discriminability; Multi-Class Scatterplot; Line Chart; Bar Chart
资金
- NSFC [61772315, 61861136012]
- Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University [VRLAB2020C08]
- CAS grant [GJHZ1862]
- Deutsche Forschungsgemeinschaft (DFG) [DE 620/26-1, 251654672 - TRR 161]
The study introduces an integrated method for creating and assigning color palettes to improve visual discrimination of classes in different visualizations. Results show that the method generates high-quality color palettes with high efficiency, and can incorporate user modifications.
We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.
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