A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Task Driven 3D Object Recognition System Using Bayesian Networks
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Bayesian Network Framework for Relational Shape Matching
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Recognition of Composite Human Activities through Context-Free Grammar Based Representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Bayesian Network Learning with Parameter Constraints
The Journal of Machine Learning Research
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Combining models of pose and dynamics for human motion recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
A characterization of the dirichlet distribution with application to learning Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Sparse flexible models of local features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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We present an Expectation-Maximization learning algorithm (E.M.) for estimating the parameters of partially-constrained Bayesian trees. The Bayesian trees considered here consist of an unconstrained subtree and a set of constrained subtrees. In this tree structure, constraints are imposed on some of the parameters of the parametrized conditional distributions, such that all conditional distributions within the same subtree share the same constraint. We propose a learning method that uses the unconstrained subtree to guide the process of discovering a set of relevant constrained substructures. Substructure discovery and constraint enforcement are simultaneously accomplished using an E.M. algorithm. We show how our tree substructure discovery method can be applied to the problem of learning representative pose models from a set of unsegmented video sequences. Our experiments demonstrate the potential of the proposed method for human motion classification.