Texture Modelling with Nested High-order Markov-Gibbs Random Fields

@article{Versteegen2016TextureMW,
  title={Texture Modelling with Nested High-order Markov-Gibbs Random Fields},
  author={Ralph Versteegen and Georgy L. Gimel'farb and Patricia Riddle},
  journal={Computer Vision and Image Understanding},
  year={2016},
  volume={143},
  pages={120-134}
}
Currently, Markov–Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family distributions nested over base models, so that potentials added later can build on previous ones. We… CONTINUE READING
Tweets
This paper has been referenced on Twitter 3 times. VIEW TWEETS

References

Publications referenced by this paper.
SHOWING 1-10 OF 68 REFERENCES

G

  • A. M. Ali, A. A. Farag
  • L. Gimel’farb, Optimizing binary MRFs with higher…
  • 2008
Highly Influential
4 Excerpts

Filters

  • S. C. Zhu, Y. Wu, D. Mumford
  • random fields and maximum entropy (FRAME…
  • 1998
Highly Influential
11 Excerpts

Similar Papers

Loading similar papers…