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Abstract: This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatically from unlabeled data. The distinguished part of this work is that it is based on unlabeled data, i.e., the training features include both useful foreground parts and background clutter and the correspondence between the parts and detected features are unknown. We use decomposable triangulated graphs to depict ... (Update)
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BibTeX entry: (Update)
Y. Song, L. Goncalves and P. Perona, "Unsupervised Learning of Human Motion Models ", Advances in Neural Information Processing Systems 14, Vancouver, Cannada, December 2001. http://citeseer.ist.psu.edu/article/song01unsupervised.html More
@misc{ song01unsupervised,
author = "Y. Song and L. Goncalves and P. Perona",
title = "Unsupervised Learning of Human Motion Models",
text = "Y. Song, L. Goncalves and P. Perona, Unsupervised Learning of Human Motion
Models , Advances in Neural Information Processing Systems 14, Vancouver,
Cannada, December 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/article/song01unsupervised.html" }
Citations (may not include all citations):2319 Elements of Information Theory (context) - Cover, Thomas - 1991
208 Approximating discrete probability distributions with depend.. (context) - Chow, Liu - 1968
110 Learning in Graphical Models (context) - Jordan - 1999
58 Unsupervised learning of models for recognition - Weber, Welling et al. - 2000
54 Detection and tracking of point features - Tomasi, Kanade - 1991
32 Towards detection of human motion - Song, Feng et al. - 2000
28 Learning with mixtures of trees - Meila, Jordan - 2000
13 Graphical templates for model registration - Amit, Kong - 1996
10 Maximum likelihood bounded tree-width markov networks - Srebro - 2001
7 Monocular perception of biological motion in johansson displ.. (context) - Song, Goncalves et al. - 2001
6 Unsupervised Learning of Models for Object Recognition (context) - Weber - 2000
4 Learning bayesian networks from data (context) - Friedman, Goldszmidt - 1998
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