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Label-Count Loss Augmented Inference
Learning Low-order Models for Enforcing High-order Statistics ============================================================= This software implements the learning approach introduced in: Patrick Pletscher & Pushmeet Kohli Learning Low-order Models for Enforcing High-order Statistics AISTATS, 2012. The paper can be downloaded from: http://pletscher.org/papers/pletscher2012hol.pdf If you use the software in your work, then please consider citing the paper as follows: @inproceedings{Pletscher2012b, author = {Pletscher, Patrick and Kohli, Pushmeet}, title = {Learning Low-order Models for Enforcing High-order Statistics}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics ({AISTATS}) 2012}, pages = {886--894}, year = {2012}, publisher = {JMLR: W\&CP 22}, address = {La Palma, Canary Islands}, editor = {Neil Lawrence and Mark Girolami} } 1. Installation --------------- The implementation splits the work into three parts: prepare, main and collect, so that things can easily be run on a cluster for different data sets and features. Most of the main logic is in grabcut/grabcutMain.m and the scripts it is calling. If you want to run the main script on a cluster, I recommend compiling it with something along the lines of the command brutus_compile.sh (note: you'll need to adapt the mosek path in this file). The step-by-step guide below describes the workflow assuming you compile the code. a. obtain mpe_inference and matluster cd grabcut git clone [email protected]:ppletscher/matluster.git git clone [email protected]:ppletscher/mpe_inference.git cd mpe_inference make b. Install the mosek toolbox in a subfolder grabcut/mosek. You need the "free academic license". I would recommend just creating a symlink to a central place where you save the mosek toolbox, such as ~/mosek. c. Obtain the images and corresponding segmentations from: http://www.robots.ox.ac.uk/~vgg/data/iseg/ Unpack the files into a grabcut/data/ folder. The folder should now contain three subfolders: images, images-gt and images-labels. Furthermore, install the gsc-1.2 software by downloading it from: http://www.robots.ox.ac.uk/~vgg/software/iseg/ and put it into grabcut/gsc-1.2. Run setup() compile_mex from within Matlab. d. In matlab go to preprocess and run mex mex_setupTransductionGraphNew.cpp e. In Matlab run the command: runPreprocessAll; f. In Matlab run the command: grabcutPrepare; g. On the command-line compile the main script: ./brutus_compile.sh h. From the command-line submit the jobs to the cluster: ./submit_grabcut.sh i. Once all the jobs have successfully completed, run the collection from within Matlab: grabcutCollect; 2. Authors ---------- The software was written by: Patrick Pletscher and Pushmeet Kohli We would like to acknowledge: - Sebastian Nowozin for input about Mosek and pruning strategies. - Aditya Khosla for general feedback. For convenience we included the gsc-1.2 package, which can be obtained from here: http://www.robots.ox.ac.uk/~vgg/research/iseg/ If you use the gsc code, please also acknowledge the relevant paper: Geodesic Star Convexity for Interactive Image Segmentation V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman at CVPR 2010
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