attribute-features is a Python wrapper around Matlab/C++ attribute-based object recognition code from http://vision.cs.uiuc.edu/attributes/.

To use the python wrapper, you need to install Octave, Octave's img package, and Python's oct2py package. Attribute features can then be extracted from image by passing the filename to attributes.detect.

In case of segmentation faults during detection, re-building MEX-files is the first thing to try. Building MEX-files requires mkoctfile Octave utility, if it's not part of default Octave package for your distribution it can be found under octave-devel or such. There are four files to build:

• feature_extraction/code/textons/anigauss_mex.c (the result .mex then needs to be linked to feature_extraction/code/textons/anigauss.mex)
• feature_extraction/code/features.c
• feature_extraction/code/getNearest_mex.c
• feature_extraction/code/resize.cc

prediction_demo.m needs to know where the VOC 2008 data is. This data is best downloaded from http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2008/VOCtrainval_14-Jul-2008.tar.