Supplementary Material + TextonBoost code

If you're using any of the material or code on this page, cite our paper Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials .

TextonBoost code:
Here is an implementation of textonboost that should give you about 83-84% on MSRC. The code is provided as is, so please don't write me about getting it to run properly. I haven't tested it on any OS but linux. It's under BSD license. TextonBoost is probably patented. In any case it should be used for research only.

How to use:

Mess with settings.h. Give it the path to the MSRC or VOC dataset.
Build it using CMake (dependencies: QT, Eigen3, TBB) [out of place building is encouraged]
Compute the textons : ./textonize_all.sh
Train the classifier: build/src/textonboost data/msrc.dat data/msrc_color.dat data/msrc_filterbank.dat data/msrc_hog_l.dat data/msrc_location.dat
Evaluate the classifier: build/src/evaluate data/msrc.dat data/msrc_color.dat data/msrc_filterbank.dat data/msrc_hog_l.dat data/msrc_location.dat path/to/a/large/harddrive

This will create a bunch of binary files that can be loaded as "Image<float>". The values are either energies or probabilities depending on the RAW_BOOSTING_OUTPUT flag (on: energies, off: probabilities).

Additional material and dataset:
We split the MSRC training data in the following way. The accurate annotations can be found here.

VOC2010:
To get textonboost to yield a decent result on VOC 2010 you need to train a bounding box classifier and use its response as a texton for texton boost. We used Discriminatively Trained Deformable Part Models.