Gauss mixture models are commonly used in image classification due to their analytical tractability and robustness. When the feature vectors are formed as the coefficients of a linear image transform, the underlying mixture components are not necessarily Gaussian, in which case there is no guarantee that the Gauss mixture model (GMM)-based clustering algorithms can capture the mixture components. In this work, we train an unbalanced tree-structured GMM-based classifier to reduce this problem. We derive and apply a parameter-independent test to determine the number of mixture components in any given tree node. The classifier tree is grown only in the regions with multiple mixture components.
['Kivanc M. Ozonat', 'Robert M. Gray']
Gauss mixture image classification for the linear image transforms