Segmentation And Classification Of Textured Images Using 2-D Stochastic Models

Abstract

In this paper, methods for supervised classification and unsupervised segmentation of textured images are presented. A class of two-dimensional, stochastic, non-causal, linear models known as Simultaneous Autoregressive (SAR) random field models is used to characterize texture in a local neighborhood N. The maximum likelihood esti-mates of the model parameters denoted by fN, are selected as textural features. An efficient method for selection of a N (i.e. order of the model) which produces powerful features is presented. It relies on visual examination and comparison of images synthesized using fN. A 08% correct classification rate is obtained in supervised experiments involving nine different types of natural textures and utiliz-ing features selected by this technique. These features are also used for unsupervised texture segmentation, i.e. divid-ing an image into regions of similar texture when no apriori knowledge about the types and number of textures in the underlying image is available. Textural edges (borders between differently textured regions) are located where sud-den changes in local textural features happen. The image is scanned by a small size window and SAR features are extracted from the region encompassed by each window. Abrupt changes in the features of neighboring windows are detected and mapped back to the spatial domain to yield the sought after textural edges. A method for automatic selection of the size of the scanning window is presented. Instead of one window, two windows whose sizes differ by a few pixels are utilized and the common resulting edges are used. Parallel implementation of the segmentation algo-rithm is discussed. The goodness of the technique is demonstrated through experimental studies.