Self-Organization of Pulse-Coupled Oscillators with Application to Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Quasi-Invariants for Human Action Representation and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Facial-component-based bag of words and PHOG descriptor for facial expression recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
Facial expression recognition in JAFFE dataset based on Gaussian process classification
IEEE Transactions on Neural Networks
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
A real-time automated system for the recognition of human facial expressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and Classification
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Machine learning for interactive systems and robots: a brief introduction
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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In this paper, we develop an efficient approach for the learning of finite Beta-Liouville mixture models. Unlike existing approaches, our is based on expectation propagation for parameters estimation and can select automatically the appropriate number of mixture components. We provide a coherent and unified learning framework to learn the complexity of the deployed mixture models and all the involved model parameters. We illustrate the performance of our learning algorithm with artificial data and a real application namely spatio-temporal objects (or dynamic events) recognition which has significant potential to be used in interactive systems or robotics. In particular, we highlight three of the most common spatio-temporal objects which involving facial expression, human activities and hand gesture. Our experiments results show the merits of the proposed approach.