Efficient Spatial Classification Using Decoupled Conditional Random Fields

  • Chi-Hoon Lee
  • Russell Greiner
  • Osmar Zaïane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


We present a discriminative method to classify data that have interdependencies in 2-D lattice. Although both Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) are well-known methods for modeling such dependencies, they are often ineffective and inefficient, respectively. This is because many of the simplifying assumptions that underlie the MRF’s efficiency compromise its accuracy. As CRFs are discriminative, they are typically more accurate than the generative MRFs. This also means their learning process is more expensive. This paper addresses this situation by defining and using “Decoupled Conditional Random Fields (DCRFs)”, a variant of CRFs whose learning process is more efficient as it decouples the tasks of learning potentials. Although our model is only guaranteed to approximate a CRF, our empirical results on synthetic/real datasets show that DCRF is essentially as accurate as other CRF variants, but is many times faster to train.


Support Vector Machine Markov Random Field Conditional Random Field Sequential Minimal Optimization Tumor Segmentation 
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

  • Chi-Hoon Lee
    • 1
  • Russell Greiner
    • 1
  • Osmar Zaïane
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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