Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Unsupervised Texture Segmentation by Spectral-Spatial-Independent Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Colour texture segmentation using modelling approach
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
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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.