Deep Colormap Extraction from Visualizations
This addresses the need for automated colormap extraction in visualization tools, but it is incremental as it builds on existing deep learning techniques with a new dataset and network module.
The paper tackles the problem of automatically extracting colormaps from visualization images using a deep learning approach, achieving superior performance over existing methods on synthetic and real-world datasets.
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.