Katugampala, Nilantha and Wilson, Roland (2003) Parameter estimation of two dimensional component Gaussian mixtures. University of Warwick. Department of Computer Science. (Department of Computer Science research report). (Unpublished)

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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.

Item Type:	Report
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Mixture distributions (Probability theory), Digital video, Akaike Information Criterion
Series Name:	Department of Computer Science research report
Publisher:	University of Warwick. Department of Computer Science
Official Date:	2003
Dates:	Date Event 2003 Published
Number:	Number 390
Number of Pages:	61
DOI:	CS-RR-390
Institution:	University of Warwick
Theses Department:	Department of Computer Science
Status:	Peer Reviewed
Publication Status:	Unpublished
Related URLs:	Publisher

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