Segmentation of monochrome and color textures using moving average modeling approach

The segmentation of textures using features extracted with a 2-D moving average (MA) modeling approach is presented. The 2-D MA model represents a texture as an output of a 2-D finite impulse response (FIR) filter with simple input process. The 2-D MA model is flexible, and can be used for modeling both isotropic and anisotropic textures. The maximum Likelihood (ML) estimators of the 2-D MA model are used as texture features. Supervised and unsupervised texture segmentation is considered. A neural network is used for supervised segmentation, and a fuzzy clustering algorithm is used for unsupervised segmentation. The texture features extracted by the 2-D MA modeling approach from sliding windows are classified with a neural network for supervised segmentation, and are clustered by a fuzzy clustering algorithm for unsupervised texture segmentation. The performance of the segmentation algorithms using MA model features are demonstrated in the experiment with both synthetic and natural images. (C) 1999 Elsevier Science B.V. All rights reserved.