Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering

R. Gaetano 1, 2 G. Scarpa 2 G. Poggi 2 J. Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes
Abstract : Tree-Structured Markov Random Field (TS-MRF) models have been recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. We propose here a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node (thus allowing for a non-binary tree), and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique. Experimental results prove the potential of the proposed method.
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Communication dans un congrès
Proc. European Signal Processing Conference, EUSIPCO 2008, Aug 2008, Lausanne (CH), Switzerland. 2008
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Contributeur : Raffaele Gaetano <>
Soumis le : vendredi 16 juillet 2010 - 18:26:06
Dernière modification le : mercredi 15 septembre 2010 - 15:03:18
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R. Gaetano, G. Scarpa, G. Poggi, J. Zerubia. Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering. Proc. European Signal Processing Conference, EUSIPCO 2008, Aug 2008, Lausanne (CH), Switzerland. 2008. <inria-00503198>

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