  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.
