Expectation-Maximization & Belief Propagation. Alan Yuille Dept. Statistics UCLA. 1. Chair. Goal of this Talk. The goal is to introduce the Expectation-Maximization (EM) and Belief Propagation (BP) algorithms.
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Dept. Statistics UCLA
Assume that images are smooth except at sharp discontinuities (edges). Justification from the statistics of real images (Zhu & Mumford).
The Graphical Model.
An undirected graph.
Hidden Markov Model.
The potential. If the gradient in u
becomes too large, then the line process
is activated and the smoothness is cut.
What do we want to estimate?
(ii) max-product converges to the maximum probability states of P(x).
But this is not very special, because other algorithms do this – see next lecture.