Name
Abstract | ||
|---|---|---|
This work presents an application of TurSOM for high-dimensional segmentation and cluster identification. TurSOM is a variation algorithm of the SOM algorithm, which introduces a new mechanism of self-organization: connection reorganization. We have theoretically presented TurSOM in very recent previous work, however, the applicability of the novel architecture is expanding as we explore it numerous advantages and possibilities. The intent of these experiments, and TurSOM itself, is to be able to segment and identify various distinct objects in color data (red, green, blue). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICSMC.2009.5346822 | San Antonio, TX |
Keywords | Field | DocType |
image colour analysis,image segmentation,pattern clustering,self-organising feature maps,variational techniques,TurSOM,cluster identification,color image segmentation,connection reorganization,self-organizing map,variation algorithm,Self-organizing Maps,Turing Unorganized Machines,clustering,segmentation,unsupervised learning | Convergence (routing),Computer vision,Segmentation,Computer science,Image segmentation,Self-organizing map,Unsupervised learning,Pixel,Color image segmentation,Artificial intelligence,Cluster analysis,Machine learning | Conference |
ISSN | ISBN | Citations |
1062-922X E-ISBN : 978-1-4244-2794-9 | 978-1-4244-2794-9 | 1 |
PageRank | References | Authors |
0.35 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Derek Beaton | 1 | 47 | 5.52 |
| Iren Valova | 2 | 136 | 25.44 |
| Daniel MacLean | 3 | 113 | 11.82 |