Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
An introduction to variational methods for graphical models
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
An Expert System for Multiple Emotional Classification of Facial Expressions
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Bayesian Feature and Model Selection for Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic complexity for mixture of exponential families in generalized variational Bayes
Theoretical Computer Science
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Variational Bayesian mixture model on a subspace of exponential family distributions
IEEE Transactions on Neural Networks
DynTex: A comprehensive database of dynamic textures
Pattern Recognition Letters
A Dynamic Texture-Based Approach to Recognition of Facial Actions and Their Temporal Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and Classification
IEEE Transactions on Knowledge and Data Engineering
Categorizing Dynamic Textures Using a Bag of Dynamical Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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During the past decade, finite mixture modeling has become a well-established technique in data analysis and clustering. This paper focus on developing a variational inference framework to learn finite Beta-Liouville mixture models that have been proposed recently as an efficient way for proportional data clustering. In contrast to the conventional expectation maximization (EM) algorithm, commonly used for learning finite mixture models, the proposed algorithm has the advantages that it is more efficient from a computational point of view and by preventing over-and under-fitting problems. Moreover, the complexity of the mixture model (i.e. the number of components) can be determined automatically and simultaneously with the parameters estimation in a closed form as part of the Bayesian inference procedure. The merits of the proposed approach are shown using both artificial data sets and two interesting and challenging real applications namely dynamic textures clustering and facial expression recognition.