Learning Large-Scale Automatic Image Colorization
Aditya Deshpande, Jason Rock and David Forsyth
University of Illinois at Urbana-Champaign
Abstract
We describe an automated method for image colorization that learns to colorize from examples. Our method exploits a LEARCH framework to train a quadratic objective function in the chromaticity maps, comparable to a Gaussian random field. The coefficients of the objective function are conditioned on image features, using a random forest. The objective function admits correlations on long spatial scales, and can control spatial error in the colorization of the image. Images are then colorized by minimizing this objective function. We demonstrate that our method strongly outperforms a natural baseline on large-scale experiments with images of real scenes using a demanding loss function. We demonstrate that learning a model that is conditioned on scene produces improved results. We show how to incorporate a desired color histogram into the objective function, and that doing so can lead to further improvements in results.
Code and Data
[CODE] [DATA]
Publications
Aditya Deshpande, Jason Rock and David Forsyth, "Learning Large-Scale Automatic Image Colorization", International Conference on Computer Vision (ICCV), 2015, Santiago, Chile.
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