The SpaCEM 3 program
The SpaCEM 3 (Spatial Clustering with EM and Markov Models) program replaces the former, still available, SEMMS (Spatial EM for Markovian Segmentation) program developed with Nathalie Peyrard from INRA Avignon.
SpaCEM 3 proposes a variety of algorithms for image segmentation, supervised and unsupervised classification of multidimensional and spatially located data. The main techniques use the EM algorithm for soft clustering and Markov Random Fields for spatial modelling. The learning and inference parts are based on recent developments based on mean field approximations. The main functionalities of the program include:
The former SEMMS functionalities, ie.
Model based unsupervised image segmentation, including the following models: Hidden Markov Random Field and mixture model;
Model selection for the Hidden Markov Random Field model;
Simulation of commonly used Hidden Markov Random Field models (Potts models).
Simulation of an independent Gaussian noise for the simulation of noisy images.
And additional possibilities such as,
New Markov models including various extensions of the Potts model and triplets Markov models;
Additional treatment of very high dimensional data using dimension reduction techniques within a classification framework;
Models and methods allowing supervised classification with new learning and test steps.
The SEMMS package, written in C, is publicly available at: http://mistis.inrialpes.fr/software/SEMMS.html . The SpaCEM 3 written in C++ is available at http://spacem3.gforge.inria.fr . Sophie Chopart started working on the initial version of the software and included a user interface and other improvements. Also we started adding the possibility to deal with mixtures of Poisson distributions in particular in the context of our application to epidemiology.