Name
Abstract | ||
|---|---|---|
In this paper we explore the use of the cluster analysis in segmentation problems, that is, identifying image points with an indication of the region or class they belong to. The proposed algorithm uses the well known agglomerative hierarchical cluster analysis algorithm in order to form clusters of pixels, but modified so as to cope with the high dimensionality of the problem. The results of different stages of the algorithm are saved, thus retaining a collection of segmented images ordered by degree of segmentation. This allows the user to view the whole collection and choose the one that suits him best for his particular application. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10268-4_21 | CIARP |
Keywords | Field | DocType |
agglomerative hierarchical cluster analysis,different stage,hierarchical clustering,dynamic image,segmented image,particular application,image point,proposed algorithm,segmentation method,cluster analysis,whole collection,segmentation problem,high dimensionality,image segmentation,growing region | Data mining,Fuzzy clustering,Canopy clustering algorithm,Scale-space segmentation,Pattern recognition,Computer science,Segmentation-based object categorization,Image segmentation,Nearest-neighbor chain algorithm,Artificial intelligence,Region growing,Single-linkage clustering | Conference |
Volume | ISSN | Citations |
5856 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Galbiati | 1 | 25 | 2.91 |
| Héctor Allende | 2 | 148 | 31.69 |
| Carlos Becerra | 3 | 2 | 2.11 |