A Computer-Assisted Colorization Approach Based on Efficient Belief Propagation and Graph Matching

  • Alexandre Noma
  • Luiz Velho
  • Roberto M. CesarJr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

Region-based approaches have been proposed to computer-assisted colorization problem, typically using shape similarity and topology relations between regions. Given a colored frame, the objective is to automatically colorize consecutive frames, minimizing the user effort to colorize the remaining regions. We propose a new colorization algorithm based on graph matching, using Belief Propagation to explore the spatial relations between sites through Markov Random Fields. Each frame is represented by a graph with each region being associated to a vertex. A colored frame is chosen as a ‘model’ and the colors are propagated to uncolored frames by computing a correspondence between regions, exploring the spatial relations between vertices, considering three types of information: adjacency, distance and orientation. Experiments are shown in order to demonstrate the importance of the spatial relations when comparing two graphs with strong deformations and with ‘topological’ differences.

Keywords

Spatial Relation Belief Propagation Graph Match Edge Attribute Colorization Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bezerra, H., Feijo, B., Velho, L.: A Computer-Assisted Colorization Algorithm based on Topological Difference. In: 19th SIBGRAPI, pp. 71–77 (2006)Google Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: Fast Approximate Energy Minimization via Graph Cuts. PAMI 23(11), 1222–1239 (2001)Google Scholar
  3. 3.
    Caelli, T., Caetano, T.: Graphical models for graph matching: approximate models and optimal algorithms. PRL 26(3), 339–346 (2005)Google Scholar
  4. 4.
    Catmull, E.: The problems of computer-assisted animation. SIGGRAPH 12(3), 348–353 (1978)CrossRefGoogle Scholar
  5. 5.
    Consularo, L.A., Cesar-Jr, R.M., Bloch, I.: Structural Image Segmentation with Interactive Model Generation. In: ICIP, vol. 6, pp. 45–48 (2007)Google Scholar
  6. 6.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty Years Of Graph Matching In Pattern Recognition. IJPRAI 18(3), 265–298 (2004)Google Scholar
  7. 7.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Belief Propagation for Early Vision. IJCV 70(1), 41–54 (2006)CrossRefGoogle Scholar
  8. 8.
    Noma, A., Pardo, A., Cesar-Jr, R.M.: Structural Matching of 2D Electrophoresis Gels using Graph Models. In: 21st SIBGRAPI, pp. 71–78 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandre Noma
    • 1
  • Luiz Velho
    • 2
  • Roberto M. CesarJr
    • 1
  1. 1.IME-USPUniversity of São PauloBrazil
  2. 2.IMPA, Instituto de Matemática Pura e AplicadaRio de JaneiroBrazil

Personalised recommendations