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Multiclass Image Labeling with Semidefinite Programming

  • Jens Keuchel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)

Abstract

We propose a semidefinite relaxation technique for multiclass image labeling problems. In this context, we consider labeling as a special case of supervised classification with a predefined number of classes and known but arbitrary dissimilarities between each image element and each class. Using Markov random fields to model pairwise relationships, this leads to a global energy minimization problem. In order to handle its combinatorial complexity, we apply Lagrangian relaxation to derive a semidefinite program, which has several advantageous properties over alternative methods like graph cuts. In particular, there are no restrictions concerning the form of the pairwise interactions, which e.g. allows us to incorporate a basic shape concept into the energy function. Based on the solution matrix of our convex relaxation, a suboptimal solution of the original labeling problem can be easily computed. Statistical ground-truth experiments and several examples of multiclass image labeling and restoration problems show that high quality solutions are obtained with this technique.

Keywords

Lagrangian Relaxation Image Element Quadratic Assignment Problem Image Label Label Problem 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jens Keuchel
    • 1
  1. 1.Institute of Computational ScienceETH ZurichZurichSwitzerland

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