"Real-Valued Graphical Models for Computer Vision"
Michael Isard, Microsoft Research
Tuesday, November 26, 2002 at 4:00 P.M.
Lubrano Conference Room
Probabilistic models have been adopted for many computer vision applications, however inference in high-dimensional spaces remains problematic. As the state-space of a model grows, the dependencies between the dimensions lead to an exponential growth in computation when performing inference. Many common computer vision problems naturally map onto the graphical model framework; the representation is a graph where each node contains a portion of the state-space and there is an edge between two nodes only if they are not conditionally independent. When this graph is sparsely connected, belief propagation algorithms can turn an exponential inference computation into one which is linear in the size of the graph. However belief propagation is only applicable when the variables in the nodes are discrete-valued or jointly represented by a single multivariate Gaussian distribution, and this rules out many computer vision applications.
I will describe a technique to combine belief propagation with ideas from particle filtering; the resulting algorithm performs inference on graphs containing both cycles and continuous-valued latent variables with general conditional probability distributions. Such graphical models have wide applicability in the computer vision domain; I have tested it on example problems of low-level edge linking and locating jointed structures in clutter.
Host: Michael Black