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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 , Laboratoire I3S - 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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Submitted on : Friday, July 16, 2010 - 6:26:06 PM
Last modification on : Thursday, August 4, 2022 - 4:52:34 PM
Long-term archiving on: : Friday, October 22, 2010 - 3:14:35 PM


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  • HAL Id : inria-00503198, version 1



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. ⟨inria-00503198⟩



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