International Journal of Computer Vision
Generalized Dirichlet distribution in Bayesian analysis
Applied Mathematics and Computation
Spatial Color Indexing and Applications
International Journal of Computer Vision
Modeling word burstiness using the Dirichlet distribution
ICML '05 Proceedings of the 22nd international conference on Machine learning
Journal of Visual Communication and Image Representation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Statistical Model for Histogram Refinement
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Discrete visual features modeling via leave-one-out likelihood estimation and applications
Journal of Visual Communication and Image Representation
A Liouville-based approach for discrete data categorization
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
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Finite mixture modeling have been applied for different data mining tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel mixture that we call the multinomial generalized Dirichlet mixture. We designed experiments involving spatial color image databases modeling and summarization to show the robustness, flexibility and merits of our approach.