Texture classification and segmentation using multiresolution simultaneous autoregressive models

Abstract

We present a multiresolution simultaneous autoregressive (MR-SAR) model for texture classification and segmentation. First, a multivariate rotation-invariant SAR (RISAR) model is introduced which is based on the circular autoregressive (CAR) model. Experiments show that the multivariate RISAR model outperforms the CAR model in texture classification. Then, we demonstrate that integrating the information extracted from multiresolution SAR models gives much better performance than single resolution methods in both texture classification and texture segmentation. A quality measure to evaluate individual features for the purpose of segmentation is also presented. We employ the spatial coordinates of the pixels as two additional features to remove small speckles in the segmented image, and carefully examine the role that the spatial features play in texture segmentation. Two internal indices are introduced to evaluate the unsupervised segmentation and to find the “true” number of segments or clusters existing in the textured image.