Name
Title | ||
|---|---|---|
An efficient unsupervised diffusion clustering algorithm with application to shape decomposition based on visibility context. |
Abstract | ||
|---|---|---|
In this paper the two and three dimensional of a single shape partitioning problem is revisited by means of an iterative, non supervised, very fast and effective algorithm. The notion of visibility context is used as the shape signature, which actually provides a physical meaning to the representation. The visibility serves as a means to manipulate on the shape parts applying clustering techniques to the corresponding graph. Therefore, the decomposition problem is re-casted as a clustering problem. An unsupervised Diffusion Clustering Algorithm is proposed, which efficiently achieves to capture the functional shape parts. Although the proposed algorithm is developed and fits very well to the specific problem of the shape partitioning, its utility is undoubtedly much more general. Experimental results conducted on two and three dimensional shape databases are very promising. Graphical abstractDisplay Omitted HighlightsDCA: a fast clustering algorithm for 2D and 3D shape decomposition.Graph based visibility is efficiently diffused, capturing nearly convex parts.Closely related to clique graphs, perceptual rule for convexity is also implied. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.image.2016.12.012 | Sig. Proc.: Image Comm. |
Keywords | Field | DocType |
Graph clustering,Shape decomposition,Visibility,Diffusion | Fuzzy clustering,Canopy clustering algorithm,Active shape model,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Clustering coefficient,Cluster analysis | Journal |
Volume | Issue | ISSN |
52 | C | 0923-5965 |
Citations | PageRank | References |
0 | 0.34 | 43 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fotini Fotopoulou | 1 | 7 | 3.48 |
| Emmanouil Z. Psarakis | 2 | 43 | 11.05 |