Perturb-and-MAP Random Fields: Reducing Random Sampling to Optimization
Perturb-and-MAP random fields try to bridge the gap between
probabilistic and deterministic approaches to MRF modeling.
We have been developing a new Perturb-and-MAP framework for random sampling in
Markov random fields (MRF). We generate a random sample from the whole field
at once by first injecting random noise to the MRF local potentials, then
finding the least energy configuration of the perturbed energy function.
By properly designing the noise injection process we can generate exact Gibbs
samples from Gaussian MRFs and good approximate samples from discrete-label
MRFs. With Perturb-and-MAP random fields we thus turn powerful deterministic energy
minimization methods into efficient probabilistic random sampling
algorithms. By completely avoiding costly MCMC, we can generate in a fraction
of a second independent random samples from million-node
networks. Applications include model parameter learning and solution
uncertainty quantification in computer vision applications.
Discrete-Label Markov Random Fields (ICCV-11 paper)
In discrete-label MRFs the nodes can take one out of possible
labels. This class of models, rooted to the classic Ising and Potts
models in statistical physics, is widely used in image analysis and computer
vision in applications such as image segmentation, stereo, and optical flow
estimation. Powerful algorithms such as graph cuts, dual decomposition, and
linear programming relaxations can efficiently find or approximate the minimum
energy configuration for many discrete-label energy models used in practice.
Perturbation design: Full and reduced order Gumbel perturbations
While the Perturb-and-MAP and Gibbs models are defined quite differently, it
turns out that there exists a particular perturbation design involving the
distribution that makes the two models exactly equivalent.
However, this full-order Gumbel perturbation is not very useful in practice as
it yields a problem of exponential size. We use instead reduced order
perturbations which inject Gumbel noise to unary or pairwise MRF potentials
and lead to perturbed energies essentially as easy to minimize as the original
unperturbed one. The resulting Perturb-and-MAP model approximates the
corresponding Gibbs MRF.
What is the most efficient way to generate in Matlab random samples from a
discrete distribution specified in terms of a probability or energy table?
Check out this.
Learning the model parameters from training data by moment matching
We learn model parameters from training data by the moment matching
rule, which approximates the maximum likelihood criterion. Similarly to the
Gibbs MRF, maximum likelihood estimation in the Perturb-and-MAP model is a
concave optimization problem with a unique maximum.
The example on the left shows how the moment matching rule learns the
parameters of an Ising model from Gibbs Ising model training data. Upon
convergence, the generated Perturb-and-MAP samples reproduce the sufficient
statistics and also visually closely resemble the training examples.
Application: Interactive image segmentation
Our goal in interactive image segmentation is to find the exact outline of an
object given a rough user annotation of the foreground and background. The
main advantages of the Perturb-and-MAP model over MAP estimation as used in
standard techniques such as
It enables learning the energy model parameters from training
It produces soft segmentation maps, thus capturing the model's
uncertainty on the result.
Still, the model still employs graph cuts for sampling and is thus essentially
as fast as MAP-based methods.
Application: Tiered scene labeling
As another application, consider the scene labeling problem, where an image of
an outdoor scene is segmented into a number of regions (such as facing left,
bottom, top, etc.) – see D. Hoiem's
geometric context web
page for more information. Further, we assume that the scene has a specific
tiered structure and also employ an energy function that encourages smoothness
in the label assignments – see P. Felzenszwalb's
page for further information on the
tiered labeling model. Under the Perturb-and-MAP model, we can draw samples by
using the efficient tiered dynamic programming MAP algorithm, which enables:
Parameter learning from training data.
Marginal MAP decisions, which minimize the pixel-wise (Hamming) loss.
Uncertainty quantification of the final segmentation result.
Gaussian Markov Random Fields (NIPS-10 paper)
Exact sampling by local perturbations in Gaussian MRFs
The standard approach to sampling from Gaussian models involves computing the
Cholesky decomposition of either the covariance or the information
matrix. However this is computationally too costly for large scale models
such as those encountered in image analysis.
We have worked on an alternative
scalable method for exact sampling in GMRFs. It amounts to locally
perturbing the GMRF potentials by adding Gaussian noise to them
, followed by computing the
mean/mode of the perturbed GMRF . This implies that GMRF sampling has the same
computational complexity as GMRF mean computation and allows us to use
efficient iterative algorithms such as preconditioned conjugate gradients,
loopy Gaussian BP, or DFT-domain techniques for rapid sampling of large-scale
Non-Gaussian continuous MRFs with sparse potentials
Most modern continuous-valued models used in signal and image analysis are
non-Gaussian, but instead use ideas from sparse signal modeling. However,
Gaussian MRFs turn out to be very useful as building blocks within sparse
models. In our work we have shown that efficient Gaussian MRF sampling can be
a key ingredient that allows both Monte-Carlo and variational Bayesian
approaches scale up to large-scale data.
G. Papandreou and A. Yuille,
Perturb-and-MAP Random Fields: Reducing Random Sampling to Optimization, with Applications in Computer Vision,
in Advanced Structured Prediction, edited by S. Nowozin, P. Gehler, J. Jancsary, and C. Lampert. MIT Press, 2014.
G. Papandreou and A. Yuille,
Perturb-and-MAP Random Fields: Using Discrete Optimization to Learn and Sample from Energy Models,
Proc. IEEE Int. Conf. on Computer Vision (ICCV-11), Barcelona, Spain, Nov. 2011.
G. Papandreou and A. Yuille,
Gaussian Sampling by Local Perturbations,
Proc. Int. Conf. on Neural Information Processing Systems (NIPS-10), Vancouver, B.C., Canada, Dec. 2010.
G. Papandreou, P. Maragos, and A. Kokaram,
Image Inpainting with a Wavelet Domain Hidden Markov Tree Model,
Proc. IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP-08), pp. 773-776, Las Vegas, NV, U.S.A., Mar.-Apr. 2008.
Our work has been supported by the U.S. Office of Naval Research under
MURI grant N000141010933; the NSF under award 0917141; the AFOSR under
grant 9550-08-1-0489; and the Korean Ministry of Education, Science, and
Technology, under the National Research Foundation WCU program R31-10008. This
is gratefully acknowledged.