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Colorization for Gray Scale Facial Image by Locality-Constrained Linear Coding

  • Yang Liang
  • Mingli Song
  • Jiajun Bu
  • Chun Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7674)

Abstract

Colorization for gray scale facial image is an important technique in various practical applications. However, the methods that have been proposed are essentially semi-automatic. In this paper, we present a new probabilistic framework based on Maximum A Posteriori (MAP) estimation to automatically transform the given gray scale facial image to corresponding color one. Firstly, the input image is divided into several patches and non-parametric Markov random field (MRF) is employed to formulate the global energy. Secondly, Locality-constrained Linear Coding (LLC) is employed to learn the color distribution for each patch. At the same time, the simulated annealing algorithm is employed to iteratively update the patches chosen by LLC to optimize the MRF by decreasing global energy cost. The experimental results demonstrate that the proposed framework is effective to colorize the gray scale facial images to corresponding color ones.

Keywords

Colorization MAP MRF LLC 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yang Liang
    • 1
  • Mingli Song
    • 1
  • Jiajun Bu
    • 1
  • Chun Chen
    • 1
  1. 1.Zhejiang Provincial Key Laboratory of Service Robot, College of Computer ScienceZhejiang UniversityChina

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