This paper is concerned with identifying and estimating the parameters of the different texture regions that comprise a textured image. A textured region here is modelled by a Markov Random Field (MRF). The MRF is parametrized by a parameter vector α , ana has a noncausal structure. We assume no a prior knowledge about the different texture regions, their associated texture parameters, or the available number of textured regions. The image is partitioned into disjoint square windows and a maximum likelihood estimate (MLE) (or a sufficient statistis) α* for α (for a fixed order model) is obtained in each window. The components of α* are viewed as features, and a as a feature vector. The windows are grouped in different texture regions based on feature selection and clustering analysis of the α* vectors in the different windows. To simplify the clustering process, the dimensionality of the feature vector is reduced via a Karhunen-Loeve decomposition of the between-to-within scatter matrix of the α* vectors. Each α* is projected onto the dominant mode (eigenvector) of the scatter matrix. The projected data is used in the clustering process. The clustering is achieved by minimizing a within group variance criterion which has been weighted by a factor that explicitly depends on the number of groups. To reduce the computational cost associated with this method, it is accompanied by a "valley method". Finally, by exploiting the asymptotic normality of the MLE, we compute the tglobal MLE α* for each textured region by properly combining the locally estimated MLE α* in the various windows that comprise the region. The global MLE α* for a region is notning but an appropriately weighted linear combination of the local MLE set {^αk^*}.

Date Published: 26 March 1986
PDF: 9 pages
Proc. SPIE 0635, Applications of Artificial Intelligence III, (26 March 1986); doi: 10.1117/12.964150

Published in SPIE Proceedings Vol. 0635:
Applications of Artificial Intelligence III
John F. Gilmore, Editor(s)