Gaussian mixture trees for one class classification in automated visual inspection

Abstract

We present Gaussian mixture trees for density estimation and one class classification. A Gaussian mixture tree is a tree, where each node is associated with a Gaussian component. Each level of the tree provides a refinement of the data description of the level above. We show how this approach is applied to one class classification and how the hierarchical structure is exploited to significantly reduce computation time to make the approach suitable for real time systems. Experiments with synthetic data and data from a visual inspection task show that our approach compares favorably to flat Gaussian mixture models as well as one class support vector machines regarding both predictive performance and computation time.