Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations

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Poco J.
Mayhua A.
Heer J.
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IEEE Computer Society
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Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations “in the wild” often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations.
This work was supported by a Paul G. Allen Family Foundation Distinguished Investigator Award and the Moore Foundation Data-Driven Discovery Investigator program. The second author gratefully acknowledges CONCYTEC for a scholarship in support of graduate studies.
Palabras clave
Visualization images, Character recognition, Color, Computer vision, Data mining, Data visualization, Feature extraction, Flow visualization, Image coding, Image processing, Information retrieval, Mapping, Optical character recognition, Signal encoding, Visualization, Automatic inference, chart understanding, Image color analysis, Interpretation errors, Optical character recognition software, redesign, Static visualizations, Color image processing