期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 28, 期 12, 页码 4048-4060出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2021.3070876
关键词
Color extraction; information visualization; deep learning; color histogram
资金
- National Natural Science Foundation of China [61802388]
- SIAT Innovation Program for Excellent Young Researchers
This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. The method summarizes colors in an input visualization image and uses a pre-trained deep neural network to predict the colormap. The approach performs well on both synthetic and real-world visualizations.
This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of similar to 64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.
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