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In this paper, we present a novel statistical approach to medical image segmentation. This approach is based on finite mixture models with spatial smoothness constrains. The main advantages of the proposed approach can be summarized as follows. Firstly, the proposed model is based on inverted Dirichlet mixture models, which have demonstrated better performance in modeling positive data (e.g., images)...
We propose model-based inference for topic novelty detection using a non-parametric Bayesian probability model. The probability model is a Dirichlet process mixture of inverted Dirichlet distributions which can be viewed as an infinite mixture model. The inference is based on variational Bayes deployed using approximate conjugate priors to the inverted Dirichlet. Detailed experimental study demonstrates...
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