Probsub

Structured SVM with probably submodular constraints.

Citation

For use, please cite:

Berman, Maxim, & Blaschko, Matthew B. (2016, December). Efficient optimization for probably submodular constraints in CRFs. In Proceedings of the NIPS workshop on constructive machine learning.

See also prior reference on the probably submodular framework

Zaremba, Wojciech, & Blaschko, Matthew B. (2016, March). Discriminative training of CRF models with probably submodular constraints. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE.

The package relies on PyStruct

Müller, Andreas C., and Sven Behnke. "PyStruct: learning structured prediction in python." Journal of Machine Learning Research 15.1 (2014): 2055-2060.

Installation

• Create environment and install requirements

  conda create --name probsubenv python=2.7 numpy cvxopt scikit-image
  conda install -c https://conda.binstar.org/menpo opencv
  source activate probsubenv
  pip install git+https://github.com/bermanmaxim/pystruct.git@hardconstraints

  Note As indicated pystruct has to be fetched from the weightedloss branch of my fork of pystruct.

• install opengm with Python and graph-cuts extensions and and make the python module available in the environment (for graph-cut inference)

Usage

• one_slack_ssvm_hard.py constains one-slack SSVM learner class for pystruct with additional hard constraints <w, a> >= b, either specified or generated with learner.generate_hard_constraints
• probsub.py provides an interface to learners with probably submodular constraints, provided by probsub_helpers.py

Project webpage

Additional information can be found on the project webpage http://homes.esat.kuleuven.be/~mblaschk/projects/learnConstraints/