ColorArt: Suggesting Colorizations for Graphic Arts Using Optimal Color-Graph MatchingDownload PDF

Apr 04, 2020 (edited May 29, 2020)Graphics Interface 2020 Conference Blind SubmissionReaders: Everyone
  • Keywords: Graphic arts, Colorization, Infographics, Color Graph Matching, Automatic colorization, Reference based colorization
  • TL;DR: Automatic coloring suggestions for graphic arts, pattern, infographics etc. using a dataset of colored reference images by artists.
  • Abstract: Colorization is a complex task of selecting a combination of colors and arriving at an appropriate spatial arrangement of the colors in an image. In this paper, we propose a novel approach for automatic colorization of graphic arts like graphic patterns, info-graphics and cartoons. Our approach uses the artist's colored graphics as a reference to color a template image. We also propose a retrieval system for selecting a relevant reference image corresponding to the given template from a dataset of reference images colored by different artists. Finally, we formulate the problem of colorization as a optimal graph matching problem over color groups in the reference and the template image. We demonstrate results on a variety of coloring tasks and evaluate our model through multiple perceptual studies. The studies show that the results generated through our model are significantly preferred by the participants over other automatic colorization methods.
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