Learning Stick-Figure Models Using Nonparametric Bayesian Priors over Trees
Abstract
We present a probabilistic stick-figure model that uses a nonparametric Bayesian distribution over trees for its structure prior. Sticks are represented by nodes in a tree in such a way that their parameter distributions are probabilistically centered around their parent node. This prior enables the inference procedures to learn multiple explanations for motion-capture data, each of which could be trees of different depth and path lengths. Thus, the algorithm can automatically determine a reasonable distribution over the number of sticks in a given dataset and their hierarchical relationships. We provide experimental results on several motion-capture datasets, demonstrating the modelpsilas ability to recover plausible stick-figure structure, and also the modelpsilas robust behavior when faced with occlusion.
Cite
Text
Meeds et al. "Learning Stick-Figure Models Using Nonparametric Bayesian Priors over Trees." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587559Markdown
[Meeds et al. "Learning Stick-Figure Models Using Nonparametric Bayesian Priors over Trees." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/meeds2008cvpr-learning/) doi:10.1109/CVPR.2008.4587559BibTeX
@inproceedings{meeds2008cvpr-learning,
title = {{Learning Stick-Figure Models Using Nonparametric Bayesian Priors over Trees}},
author = {Meeds, Edward and Ross, David A. and Zemel, Richard S. and Roweis, Sam T.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587559},
url = {https://mlanthology.org/cvpr/2008/meeds2008cvpr-learning/}
}