In this work a novel texture model particularly suited for unsupervised image segmentation is proposed. Any texture is represented
at region level by means of a finite-state hierarchical model resulting from the superposition of several Markov chains, each
associated with a different spatial direction. Corresponding to such a modeling, an optimization scheme, referred to as Texture
Fragmentation and Reconstruction (TFR) algorithm, has been introduced.
The TFR addresses the model estimation problem in two sequential layers: the former “fragmentation” step allows to find the
terminal states of the model, while the latter “reconstruction” step is aimed at estimating the relationships among the states
which provide the optimal hierarchical structure to associate with the model. The latter step is based on a probabilistic
measure, i.e, the region gain, which accounts for both the region scale and the inter-region interaction.
The proposed segmentation algorithm was tested on a segmentation benchmark and applied to high resolution remote-sensing forest
images as well.
Keywords Segmentation - texture model - Markov chain - remote sensing - forest classification