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Discriminative fields for modeling spatial dependencies in natural images (2003)

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by Sanjiv Kumar , Martial Hebert
Venue:In NIPS
Citations:86 - 2 self
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BibTeX

@INPROCEEDINGS{Kumar03discriminativefields,
    author = {Sanjiv Kumar and Martial Hebert},
    title = {Discriminative fields for modeling spatial dependencies in natural images},
    booktitle = {In NIPS},
    year = {2003},
    publisher = {MIT Press}
}

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Abstract

In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classification problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments. 1

Citations

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944 J: General Linear Models - McCullagh, Nelder - 1989
424 What energy functions can be minimized via graph cuts - Kolmogorov, Zabih
353 Training products of experts by minimizing contrastive divergence - Hinton
340 BDiscriminative training methods for hidden Markov models - Collins
233 Exact maximum a posteriori estimation for binary images - Greig, Porteous, et al. - 1989
196 Markov Random Field Modeling in Image Analysis - Li - 2001
133 Discriminative random fields: A discriminative framework for contextual interaction in classification - Kumar, Hebert - 2003
57 T.: Discriminative vs informative learning - Rubinstein, Hastie - 1997
44 Man-made structure detection in natural images using a causal multiscale random field - Kumar, Herbert - 2003
42 Combining belief networks and neural network for scene segmentation - Feng, Williams, et al.
41 Multiscale Bayesian Segmentation Using a Trainable Context Model - Cheng, Bouman - 2001
29 Adaptive sparseness using Jeffreys prior - Figueiredo - 2001
24 A class of discrete multiresolution random fields and its application to image segmentation - Wilson, Li - 2002
16 Bayesian non-linear modelling for the 1993 energy prediction competition - MacKay - 1996
2 Bayesian regularization and pruning using a laplacian prior - Williams - 1995
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