Mixture Models and Applications

1. Front Matter

   Pages i-xii

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2. Gaussian-Based Models

   1. Front Matter

      Pages 1-1

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   2. A Gaussian Mixture Model Approach to Classifying Response Types

      • Owen E. Parsons

      Pages 3-22

   3. Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures

      • Daniel Berio, Frederic Fol Leymarie, Sylvain Calinon

      Pages 23-38

   4. Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series

      • Sylvain Calinon

      Pages 39-57

3. Generalized Gaussian-Based Models

   1. Front Matter

      Pages 59-59

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   2. Multivariate Bounded Asymmetric Gaussian Mixture Model

      • Muhammad Azam, Basim Alghabashi, Nizar Bouguila

      Pages 61-80

   3. Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions

      • Fatma Najar, Sami Bourouis, Rula Al-Azawi, Ali Al-Badi

      Pages 81-106

4. Spherical and Count Data Clustering

   1. Front Matter

      Pages 107-107

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   2. L ₂ Normalized Data Clustering Through the Dirichlet Process Mixture Model of von Mises Distributions with Localized Feature Selection

      • Wentao Fan, Nizar Bouguila, Yewang Chen, Ziyi Chen

      Pages 109-123

   3. Deriving Probabilistic SVM Kernels from Exponential Family Approximations to Multivariate Distributions for Count Data

      • Nuha Zamzami, Nizar Bouguila

      Pages 125-153

   4. Toward an Efficient Computation of Log-Likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering

      • Masoud Daghyani, Nuha Zamzami, Nizar Bouguila

      Pages 155-176

5. Bounded and Semi-bounded Data Clustering

   1. Front Matter

      Pages 177-177

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   2. A Frequentist Inference Method Based on Finite Bivariate and Multivariate Beta Mixture Models

      • Narges Manouchehri, Nizar Bouguila

      Pages 179-208

   3. Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting

      • Kamal Maanicshah, Muhammad Azam, Hieu Nguyen, Nizar Bouguila, Wentao Fan

      Pages 209-233

   4. Online Variational Learning for Medical Image Data Clustering

      • Meeta Kalra, Michael Osadebey, Nizar Bouguila, Marius Pedersen, Wentao Fan

      Pages 235-269

6. Image Modeling and Segmentation

   1. Front Matter

      Pages 271-271

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   2. Color Image Segmentation Using Semi-bounded Finite Mixture Models by Incorporating Mean Templates

      • Jaspreet Singh Kalsi, Muhammad Azam, Nizar Bouguila

      Pages 273-305

   3. Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models

      • Wenmin Chen, Wentao Fan, Nizar Bouguila, Bineng Zhong

      Pages 307-324

   4. Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation

      • Ines Channoufi, Fatma Najar, Sami Bourouis, Muhammad Azam, Alrence S. Halibas, Roobaea Alroobaea et al.

      Pages 325-348

This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature.

• Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection;
• Present theoretical and practical developments in mixture-based modeling and their importance in different applications;
• Discusses perspectives and challenging future works related tomixture modeling.

• Finite mixture models
• Infinite mixture models
• Bayesian/variational learning
• Nonparametric Bayesian approaches
• Subspace mixture models
• Outliers detection
• High-dimensional data
• Deep mixture models
• Unsupervised learning
• Semi-supervised learning

“This book can be taken as a review of the subject. It is also a very good starting point for understanding mixture modeling and even for setting up new research. I strongly recommend this work for researchers and advanced undergraduate and graduate students of computer science and applied probability.” (Arturo Ortiz-Tapia, Computing Reviews, January 18, 2021)

“[T]his … is … a very good starting point for understanding mixture modeling and even for setting up new research. I strongly recommend this work for researchers and advanced undergraduate and graduate students of computer science and applied probability.” (Computing Reviews)

• Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada

  Nizar Bouguila

• Department of Computer Science and Technology, Huaqiao University, Xiamen, China

  Wentao Fan

Nizar Bouguila received the engineer degree from the University of Tunis, Tunis, Tunisia, in 2000, and the M.Sc. and Ph.D. degrees in computer science from Sherbrooke University, Sherbrooke, QC, Canada, in 2002 and 2006, respectively. He is currently a Professor with the Concordia Institute for Information Systems Engineering (CIISE) at Concordia University, Montreal, Quebec, Canada. His research interests include image processing, machine learning, data mining, computer vision, and pattern recognition. Prof. Bouguila received the best Ph.D Thesis Award in Engineering and Natural Sciences from Sherbrooke University in 2007. He was awarded the prestigious Prix d’excellence de l’association des doyens des etudes superieures au Quebec (best Ph.D Thesis Award in Engineering and Natural Sciences in Quebec), and was a runner-up for the prestigious NSERC doctoral prize. He is the author or co-author of more than 200 publications in several prestigious journals and conferences. Heis a regular reviewer for many international journals and serving as associate editor for several journals such as Pattern Recognition. Dr. Bouguila is a licensed Professional Engineer registered in Ontario, and a Senior Member of the IEEE. He is the holder of the Concordia University Research Chair.


Wentao Fan received his M.Sc. and Ph.D. degrees in electrical and computer engineering from Concordia University, Montreal, Quebec, Canada, in 2009 and 2014, respectively. He is currently an Associate Professor in the Department of Computer Science and Technology, Huaqiao University, Xiamen, China. His research interests include machine learning, computer vision, deep learning and pattern recognition.

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