Markov random field modeling via neural networks

[Jenq-Neng Hwang]

Technical Report available from neuroprose:

TEXTURED IMAGE SYNTHESIS AND SEGMENTATION VIA NEURAL NETWORK
PROBABILISTIC  MODELING

Jenq-Neng Hwang,  Eric Tsung-Yen Chen 

Information Processing Laboratory 
Department of Electrical Engineering, FT-10 
University of Washington, Seattle, WA 98195 


ABSTRACT

It has been shown that a trained back-propagation neural network
(BPNN) classifier with Kullback-Leibler criterion produces outputs
which   can be interpreted as estimates of Bayesian "a posteriori"
probabilities. Based on this interpretation,  we propose a
back-propagation  neural network (BPNN) approach    for the estimation
of the local conditional distributions of  textured images, which are
commonly represented by a Markov random field (MRF)  formulation.
The proposed BPNN approach overcomes many of the difficulties
encountered in using  MRF formulation.  In particular our approach
does not require the trial-and-error selection of clique functions or
the subsequent laborious and unreliable estimation of clique
parameters. Simulations show that the images synthesized using BPNN 
modeling produced desired artificial/real textures more consistently  
than MRF based methods. Application of the proposed BPNN approach to
segmentation of artificial and real-world textures  is also presented.

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