Name
Title | ||
|---|---|---|
UNSUPERVISED HIERARCHICAL IMAGE SEGMENTATION BASED ON THE TS-MRF MODEL AND FAST MEAN-SHIFT CLUSTERING |
Abstract | ||
|---|---|---|
Tree-Structured Markov Random Field (TS-MRF) models have been recently proposed to provide a hierarchical mul- tiscale 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 segmen- tation 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 al- gorithm based on the Mean-Shift technique. Experimental results prove the potential of the proposed method. |
Year | Venue | Field |
|---|---|---|
2008 | EUSIPCO | Canopy clustering algorithm,Scale-space segmentation,Pattern recognition,Markov random field,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Tree structure,Cluster analysis,Minimum spanning tree-based segmentation,Mathematics |
DocType | Citations | PageRank |
Conference | 2 | 0.40 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raffaele Gaetano | 1 | 118 | 17.28 |
| Giuseppe Scarpa | 2 | 204 | 23.23 |
| Giovanni Poggi | 3 | 655 | 53.64 |
| Josiane Zerubia | 4 | 2032 | 232.91 |