GMRF models and wavelet decomposition for texture segmentation

Abstract

A multichannel scheme for texture segmentation using Gauss Markov random fields (GMRF) is presented. We present a family of filters called Markov filters with a property that, when a homogeneous GMRF is filtered with these filters, the resulting output is also a GMRF. We use these filters to decompose images in a fashion similar to the wavelet decomposition, such that the individual subbands are also GMRFs. However, in wavelet decomposition, after filtering, the individual subbands are subsampled and Markov fields lose the Markov property when subsampled. We have shown in [Krishnamachari and Chellappa, 1995] that subsampled GMRFs can be efficiently approximated by Markov fields using the local conditional distribution invariance approximation. Hence individual subbands can be modeled by GMRFs. We have used this multichannel model to classify remote sensed imagery and to perform texture segmentation.