2,123 research outputs found
Variational approximation for mixtures of linear mixed models
Mixtures of linear mixed models (MLMMs) are useful for clustering grouped
data and can be estimated by likelihood maximization through the EM algorithm.
The conventional approach to determining a suitable number of components is to
compare different mixture models using penalized log-likelihood criteria such
as BIC.We propose fitting MLMMs with variational methods which can perform
parameter estimation and model selection simultaneously. A variational
approximation is described where the variational lower bound and parameter
updates are in closed form, allowing fast evaluation. A new variational greedy
algorithm is developed for model selection and learning of the mixture
components. This approach allows an automatic initialization of the algorithm
and returns a plausible number of mixture components automatically. In cases of
weak identifiability of certain model parameters, we use hierarchical centering
to reparametrize the model and show empirically that there is a gain in
efficiency by variational algorithms similar to that in MCMC algorithms.
Related to this, we prove that the approximate rate of convergence of
variational algorithms by Gaussian approximation is equal to that of the
corresponding Gibbs sampler which suggests that reparametrizations can lead to
improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG
Colour Texture analysis
This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The main advantage of this approach is the analysis of the textured images at a micro-level using the local distribution of the LBP values, and in the colour domain by analysing the local colour distribution obtained after colour segmentation. The use of the colour and texture information separately has proven to be inappropriate for natural images as they are generally heterogeneous with respect to colour and texture characteristics. Thus, the main problem is to use the colour and texture information in a joint descriptor that can adapt to the local properties of the image under analysis. We will review existing approaches to colour and texture analysis as well as illustrating how our approach can be successfully applied to a range of applications including the segmentation of natural images, medical imaging and product inspection
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