Last updated 18 May 2006.

How to use the DRF dataset with the CRF2D toolbox

This page describes how to load the datasets used in the following paper:

S. Kumar and M. Hebert. Discriminative Fields for Modeling Spatial Dependencies in Natural Images. Advances in Neural Information Processing Systems, NIPS 16, 2004.

This allows one to replicate the experiments performed in:

S. Vishwanathan, N. Schraudolph, M. Schmidt, K. Murphy. Accelerated Training of Conditional Random Fields with Stochastic Meta-Descent ICML'06 (Intl Conf on Machine Learning)

Note that our definition of local evidence features is slightly different from Kumar and Herbert (who use log(sigmoid(w^T x)). Also, their inference and learning procedures are different than ours. But the results are qualitatively similar.

Binary Denoising Data

Man Made Structure Detection Data

Note: this section requires code for computing feature vectors from raw images. We use matlab/C code written by Tim Rees for his CS532C class project in Fall 2004; click here for his final report and here for his feature code. (Tim used pseudo likelihood for training.)

Reproducing the ICML06 Experiments

After the data has been placed in an appropriate location and features extracted for the man-made data, the experiments (and variants) from the ICML06 paper can be ran using some additional scripts downloadable here.

The main script in this archive is called 'run.m', it takes four arguments (see the paper for details):
  • dataName: either 'manMade' or 'denoise'
  • objective: either 'PL', 'MF', or 'LBP'
  • method: either 'BFGS', 'SGD', 'ASGD', or 'SMD'
  • outFile: the name of the .mat file that will store the paths of the training negative log-likelihood and test errors (Figures 4+6).

    You should get results like the following on the digit denoising

    The legend is here

    You should get results like the following on the manmade building patch classification task