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.
翻译:这项工作基于从可视化中自动提取色图的深度学习, 提出了一个基于从可视化中自动提取色图的新方法。 在将一个输入的可视化图像中的颜色作为实验室的彩色直方图进行总结后, 我们将直方图转换到一个经过预先训练的深神经网络, 该网络可以预测产生可视化的色图。 为了培训网络, 我们创建了一个64K的新的可视化数据集, 涵盖广泛的数据分布、 图表类型和色图。 网络采用了一个原始的空间金字塔集合模块, 以在输入色直方图中的多个尺度上捕捉到颜色特征。 我们然后将预测的色图分类为离散或连续的, 并根据它的颜色直方图对预测色图进行精细化。 与现有方法的定量比较显示我们在合成和现实世界的可视化方法上的优异性表现。 我们进一步展示了我们方法在两种使用的例子中的实用性, 即颜色传输和颜色再绘图。