Unsupervised texture segmentation using a statistical wavelet-based hierarchical multidata model

In this paper, we describe a new hidden Markov random field model, which we call hierarchical multi-data model, and which is based on a triplet of random fields (two hidden random fields and one observed field) in order to capture inter-scale and within-scale dependencies between various scales of resolution of wavelet-based texture features. We present a variation of the Iterated Conditional Modes (ICM) algorithm for the segmentation, and an adaptation of the Iterative Conditional Estimation (ICE) procedure for the estimation of the statistical parameters of the model. Results of tests performed on 75 mosaics of Brodatz textures are reported.