Probabilistic PCA and ICA Subspace Mixture Models for Image Segmentation

High-dimensional data, such as images represented as points in the space spanned by their pixel values, can often be described in a significantly smaller number of dimensions than the original. One of the ways of finding lowdimensional representations is to train a mixture model of principal component analysers (PCA) on the data. However, some types of data do not fulfill the assumptions of PCA, calling for application of different subspace methods. One such a method is ICA, which has been shown in recent years to be able to find interesting basis vectors (features) in signal and image data. In this paper, a mixture model of ICA subspaces is developed similar to a mixture model of PCA subspaces proposed by others. The new algorithm is applied to a natural texture segmentation problem and is shown to give encouraging results.

• Type: Conference
• Year: 2000
• Where: BMVC
• Authors: Dick de Ridder, Josef Kittler, Robert P. W. Duin
• Keywords: BMVC 2000, Computer Vision, Mixture Model, PCA Subspaces, Principal Component Analysers