We describe a new method for computing approximations to the marginal probability mass function of the random variables in a Markov random field (MRF). When applied to the a posteriori MRF, this yields approximations to the conditional marginal probability mass function, which is the key quantity in a Bayesian classifier. We apply these ideas to an optical agricultural remote sensing problem where they outperform the pixel-by-pixel ML classifier by 38%.

Date Published: 21 September 1994
PDF: 8 pages
Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186567

Published in SPIE Proceedings Vol. 2298:
Applications of Digital Image Processing XVII
Andrew G. Tescher, Editor(s)