Paper Info

Title | ||
---|---|---|

Factorial Markov Random Fields |

Abstract | ||
---|---|---|

In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension, which we call a Factorial MRF (FMRF), is analogous to the extension from Hidden Markov Models (HMM's) to Factorial HMM's. We present an efficient EM-based algorithm for inference on Factorial MRF's. Our algorithm makes use of the fact that layers are a priori independent, and that layers only interact through the observable image. The algorithm iterates between wide inference, i.e., inference within each layer for the entire set of pixels, and deep inference, i.e., inference through the layers for each single pixel. The efficiency of our method is partly due to the use of graph cuts for binary segmentation, which is part of the wide inference step. We show experimental results for both real and synthetic images. |

Year | DOI | Venue |
---|---|---|

2002 | 10.1007/3-540-47977-5_21 | ECCV (3) |

Keywords | Field | DocType |

binary segmentation,hidden markov models,efficient em-based algorithm,factorial mrf,deep inference,wide inference step,factorial markov random fields,algorithm iterates,wide inference,standard markov random field,factorial hmm,graphical model,hidden markov model,bayesian inference,graph cut | Variable elimination,Bayesian inference,Pattern recognition,Computer science,Markov model,Markov random field,Inference,Markov chain,Variable-order Markov model,Artificial intelligence,Hidden Markov model,Machine learning | Conference |

Volume | ISSN | ISBN |

2352 | 0302-9743 | 3-540-43746-0 |

Citations | PageRank | References |

13 | 1.56 | 13 |

Authors | ||

2 |

Authors (2 rows)

Cited by (13 rows)

References (13 rows)

Name | Order | Citations | PageRank |
---|---|---|---|

Junhwan Kim | 1 | 324 | 31.96 |

Ramin Zabih | 2 | 12976 | 982.19 |