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NIPS
2003
15 years 1 months ago
Discriminative Fields for Modeling Spatial Dependencies in Natural Images
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial depe...
Sanjiv Kumar, Martial Hebert
ICCV
2011
IEEE
13 years 11 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
ICMCS
2009
IEEE
415views Multimedia» more  ICMCS 2009»
14 years 9 months ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel label...
Xiaofeng Wang, Xiao-Ping Zhang
EMMCVPR
1999
Springer
15 years 4 months ago
Markov Random Field Modelling of fMRI Data Using a Mean Field EM-algorithm
This paper considers the use of the EM-algorithm, combined with mean field theory, for parameter estimation in Markov random field models from unlabelled data. Special attention ...
Markus Svensén, Frithjof Kruggel, D. Yves v...
CVPR
2008
IEEE
16 years 1 months ago
Consistent image analogies using semi-supervised learning
In this paper we study the following problem: given two source images A and A , and a target image B, can we learn to synthesize a new image B which relates to B in the same way t...
Li Cheng, S. V. N. Vishwanathan, Xinhua Zhang