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Research Report CS-RR-390

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N. Katugampala and R.G. Wilson, Parameter Estimation of Two Dimensional Component Gaussian Mixtures (July 10, 2002).

Abstract

Multiresolution Gaussian Mixture Models (MGMM) can be used to represent image and video data in video annotation and retrieval. Preliminary experiments were carried out to estimate the model parameters for two-dimensional data. An iterative algorithm similar to Expectation-Maximisation (EM) is used to estimate the model parameters. The suitability of Akaike's Information Criterion (AIC) as a measure of model fit is also evaluated. AIC was successful for most of the synthetic data sets used in the experiments, however further work is required to develop a more consistent criterion for model fit.

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cs-rr-390.pdf

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