Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics

Authors:
Frank Dellaert;Vivek Kwatra;Sang Min Oh
Affiliations:
Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Year:
2005

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Abstract

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 data-structure 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.