(Georgia Institute of Technology, 2005-06)
Dellaert, Frank; Kwatra, Vivek; Oh, Sang Min
We introduce mixture trees, a tree-based data-structure
for modeling joint probability densities using a greedy hierarchical
density estimation scheme. We show that the mixture
tree models data efficiently at multiple resolutions, and
present fast conditional sampling as one of many possible
applications. In particular, the development of this datastructure
was spurred by a multi-target tracking application,
where memory-based motion modeling calls for fast
conditional sampling from large empirical densities. However,
it is also suited to applications such as texture synthesis,
where conditional densities play a central role. Results
will be presented for both these applications.