Skip to content
A trust-region Newton method for MAP inference in Markov Random Fields (MRFs).
C++ Other
  1. C++ 99.3%
  2. Other 0.7%
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
ICFS.cpp
ICFS.h
ICFS.o
Makefile
README.md
cliqSPSparse.hpp
cliqSPSparseEnergy.hpp
cliqSPSparseFista.hpp
cliqSPSparseGradEnergy.hpp
cliqSPSparseHessian.hpp
cliqSPSparseSCD.hpp
config.txt
dualSys.cpp
dualSys.hpp
dualSys.o
dualSysPerf.cpp
hessVecMult.hpp
makefile.uai
myUtils.cpp
myUtils.hpp
myUtils.o
newtonTest
newtonTest.cpp
newtonTest.o
polyInterp.h
quasiNewton.cpp
quasiNewton.hpp
quasiNewton.o
subProblem.hpp
variousEnergies.hpp

README.md

TRN-MRF

A trust-region Newton method for MAP inference in Markov Random Fields (MRFs).

Kindly cite the following paper in case you are using the code.

H. Kannan, N. Komodakis, N. Paragios Newton-type Methods for Inference in Higher-Order Markov Random Fields (poster) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

The additional libraries required are:

  1. opengm.
  2. eigen.
  3. openmp.

The executable (newtonTest) is created by running the Makefile.

The executable is used along with a config file. An example config file, config.txt is present in the project.

The fields in the config file are explained below:

  1. Input type

There are three ways to specify the input graphical model. Specified as hdf5 or uai or code.

i.e, using a hdf5 file or an uai file or by using energy functions (defined in the file variousEnergies.hpp).

The preferable way to do it is hdf5 because it can represent sparse clique energies, shared clique energies etc.

e.g., Input type = hdf5

  1. Input name

It can be the name of the hdf5 or uai file or the input image (as a txt file) for code based input.

e.g., Input name = graph_match_1.h5

  1. Stereo right image

Useful for code based input only. Used for specifying the other image (as a txt file) in a stereo pair.

e.g., Stereo right image = tsukubaRight.txt

  1. No. of rows

  2. No. of cols

No need to specify if hdf5 based input.

No. of rows and columns of a grid topology based graphical model.

e.g., No. of rows = 144 No. of cols = 192

  1. No. of labels

No need to specify if hdf5 based input.

No. of labels at one node. Assuming all nodes have same number of labels.

e.g., No. of labels = 16

  1. Clique row size

  2. Clique col size

No need to specify if hdf5 based input.

Clique dimensions in a grid topology based graphical model.

e.g., Clique row size = 1 Clique col size = 3

  1. Clique stride

No need to specify if hdf5 based input.

The stride with which the cliques are formed within a grid topology based graphical model.

e.g., Clique stride = 1

  1. tau

The smoothing parameter.

The value specified here will be either the beginning value within an annealing schedule or the fixed value of the smoothing, if annealing is disabled.

e.g, tau = 1

  1. Max. iterations

Maximum number of iterations for which the algorithms are run.

e.g., Max. iterations = 1000

  1. Algorithm

Name of the algorithm.

The options are trn, fista, scd, ad3, dd.

Note: ad3 and dd are called from opengm.

e.g., Algorithm = trn

  1. Sparse

Flag indicating whether the clique energies are sparse or not.

This is important when the number of labels is large, to save space.

e.g., Sparse = true

  1. Anneal = true

Flag indicating whether smoothing is annealed or not.

e.g., Anneal = true

  1. Log label

A string that is appended to the output name.

e.g., Log label = opengm_dd

  1. One clique table

Flag indicating whether all the cliques share one table of values or each of them have unique tables.

e.g., One clique table = false

  1. MRF model

Useful only when specifying code based input.

String indicating the type of clique energy. Please, look into newtonTest.cpp for understanding this point.

e.g., MRF model = highstereo

You can’t perform that action at this time.