Image segmentation using a mixture of principal components representation

In previous work, the authors have presented a new adaptive approach to image compression using a neural network-based scheme. It is based on a mixture of principal components model for data representation. The classifier used in the adaptation is a linear subspace classifier, which the authors apply to the problem of segmentation. An important property of this classifier is its insensitivity to the norm of the input vectors. As a result, regions in an image that differ only in variations in illumination are classified the same. When trained on an image, the networks extracted perceptually important features in an entirely self-organising manner. The topological ordering of the classes resulted in like classes being close together in a manner analogous to the ordering of directionally sensitive columns in the visual cortex. The classification of similar features is consistent across an image quite different to the one used in training. In addition, the segmentation is shown to be independent of variations in illumination.