<title>Multiscale autoregressive image representation for texture segmentation</title>

Abstract

Texture segmentation or modeling plays an important role in image segmentation. In this paper, we investigate multiscale autoregressive representation for texture modeling and segmentation. The proposed algorithm also uses a multiresolution decomposition of an image, and fits an AR model to each image of the multiresolution pyramid. The set of AR coefficient vectors, one at each level, defines a model for a texture and this model is used as a predictor for the segmentation process. AR coefficient vectors are used to generate a prediction of the image pyramid, from which the prediction of the image to model is built. The resulting prediction error is used to discriminate textures in a segmentation algorithm. In the proposed structure, feedback can be included between pyramid levels by adding the prediction error at he previous level to the current level before an AR model fitting. M-AR can therefore be used as a predictor like an AR model. This is different from previous multiscale approaches for which data is used at each scale for the segmentation. Since we do not need to link data from different scales, this simplifies model processing for segmentation. The estimation error of the proposed multiscale AR approach has lower variance than that of an AR model, and is less correlated. Segmentation results also show M-AR to be an improvement to AR modeling.