Unsupervised estimation of image textures using an autoregressive model

A method of estimating both the number and type of textures in an image is proposed. An autoregressive (AR) texture model is used since it describes spatial behavior in addition to mean and local variation. Solution criteria are formulated based on the concurrent estimation of the number of textures, the texture parameters, and the class of texture samples. This approach avoids problems with the instability of maximum likelihood estimation and results in an algorithm which is composed of three basic operations. The first two operations alternately reestimate the texture parameters and repartition the data into the clusters corresponding to individual textures. The third operation agglomerates clusters to reduce the number of distinct textures. Each operation attempts to minimize the basic solution criteria