Learning Representations for Automatic Colorization

  • Gustav Larsson
  • Michael Maire
  • Gregory Shakhnarovich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9908)

Abstract

We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation. On both fully and partially automatic colorization tasks, we outperform existing methods. We also explore colorization as a vehicle for self-supervised visual representation learning.

Supplementary material

419976_1_En_35_MOESM1_ESM.pdf (6.8 mb)
Supplementary material 1 (pdf 6918 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Gustav Larsson
    • 1
  • Michael Maire
    • 2
  • Gregory Shakhnarovich
    • 2
  1. 1.University of ChicagoChicagoUSA
  2. 2.Toyota Technological Institute at ChicagoChicagoUSA

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