Segmentation of monochrome and color textures using moving average modeling approach

The segmentation of textures using features extracted with 2-D moving average (MA) modeling approach is presented in this paper. The 2-D MA model represents a texture as an output of a 2-D nite impulse response (FIR) lter with simple input process. The 2-D MA model is exible, 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 2-D MA modeling approach from sliding windows are classiied 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.