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

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.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Frank Dellaert, Vivek Kwatra, Sang Min Oh
• Keywords: Computer Vision, Conditional Densities, CVPR 2005, Fast Conditional Sampling, Joint Probability Densities, Large Empirical Densities, Memory-based Motion Modeling