Modelling Profiles with a Mixture of Gaussians

Point Distribution Models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a Principle Component Analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be substantially inaccurate. In this paper, we examine how the specificity of a model can be improved by using a mixture of Gaussians, trained with the ExpectationMaximization algorithm, with reference to hand and vehicle profiles.

• Type: Conference
• Year: 2000
• Where: ICIP
• Authors: James Orwell, Darrel Greenhill, Jonathan D. Rymel, Graeme A. Jones
• Keywords: ICIP 2000, Multivariate Gaussian Model, Point Distribution Models, Principle Component Analysis