Texture classification using nonparametric Markov random fields
We present a nonparametric Markov random field model for classifying texture in images. This model can capture the characteristics of a wide variety of textures, varying from the highly structured to the stochastic. The power of our modelling technique is evident in that only a small training image is required, even when the training texture contains long range characteristics. We show how this model can be used for unsupervised segmentation and classification of images containing textures for which we have no prior knowledge of the constituent texture types. This technique can therefore be used to find a specific texture in a background of unknown textures.