This work 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 ~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.

中文翻译：

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从可视化中提取深色图。

这项工作提出了一种基于深度学习的新方法，可以从可视化中自动提取颜色图。将输入的可视化图像中的颜色汇总为Lab颜色直方图后，我们将直方图传递给预先训练的深度神经网络，该网络将学习预测生成可视化的颜色图。为了训练网络，我们创建了一个约64K可视化的新数据集，涵盖了各种各样的数据分布，图表类型和色图。该网络采用了一个空洞的空间金字塔池化模块，以捕获输入颜色直方图中多个比例的颜色特征。然后，我们将预测色图分类为离散色或连续色，并根据其颜色直方图对预测色图进行细化。与现有方法的定量比较表明，我们的方法在合成可视化和实际可视化上均具有出色的性能。我们通过两个用例（即颜色转移和颜色重新映射）进一步证明了我们方法的实用性。