ML Anthology

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

Frank Dellaert, Vivek Kwatra, Sang Min Oh
CVPR 2005 pp. 619-624
doi:10.1109/CVPR.2005.224 /cvpr/2005/dellaert2005cvpr-mixture/

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 are presented for both these applications.

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Cite

Text

Dellaert et al. "Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.224

Markdown

[Dellaert et al. "Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/dellaert2005cvpr-mixture/) doi:10.1109/CVPR.2005.224

BibTeX

@inproceedings{dellaert2005cvpr-mixture,
  title     = {{Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics}},
  author    = {Dellaert, Frank and Kwatra, Vivek and Oh, Sang Min},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {619-624},
  doi       = {10.1109/CVPR.2005.224},
  url       = {https://mlanthology.org/cvpr/2005/dellaert2005cvpr-mixture/}
}