New preprint in neuroprose

[luttrell@signal.dra.hmg.gb]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/luttrell.part-mixture.ps.Z

The file luttrell.part-mixture.ps.Z is now available for copying from the Neuroprose
repository (22 pages). This paper has been submitted to a Special Issue of IEE
Proceedings on Vision, Image and Signal Processing. An early version of this paper
appeared in the Proceedings of the IEE International Conference on Artificial
Neural Networks, Brighton, 1993, pp. 313-316.

                        The Partitioned Mixture Distribution:
             An Adaptive Bayesian Network for Low-Level Image Processing

                               Steve P Luttrell
                       Adaptive Systems Theory Section
                           Defence Research Agency
                  Malvern, Worcs, United Kingdom, WR14 3PS
                      e-mail: luttrell at signal.dra.hmg.gb

                                   ABSTRACT
Bayesian methods are used to analyse the problem of training a model to make 
predictions about the probability distribution of data that has yet to be received. 
Mixture distributions emerge naturally from this framework, but are not well-matched 
to high-dimensional problems such as image processing. An extension, called a 
partitioned mixture distribution (PMD) is presented, which is essentially a set of 
overlapping mixture distributions. An expectation-maximisation training algorithm is 
derived. Finally, the results of some numerical simulations are presented, which 
demonstrate that lateral inhibition arises naturally in PMDs, and that the nodes in a
PMD co-operate in such a way that each mixture distribution in the PMD receives the
necessary complement of machinery for it to compute its mixture distribution.