Name
Abstract | ||
|---|---|---|
In this paper we present a parameter optimisation procedure that is designed to automatically initialise the number of clusters and the initial colour prototypes required by data space partitioning techniques. The proposed optimisation approach involves a colour saliency measure used in conjunction with a SOM classification procedure. For evaluation purposes, we have integrated the proposed initialisation technique in an unsupervised colour segmentation scheme based on K-Means clustering and the evaluation has been carried out in the context of the unsupervised segmentation of natural images. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5414039 | Image Processing |
Keywords | Field | DocType |
image colour analysis,image segmentation,self-organising feature maps,unsupervised learning,K-means clustering,SOM classification procedure,adaptive colour segmentation,colour saliency,data space partitioning techniques,parameter optimisation,self-organising maps,unsupervised colour segmentation scheme,Colour saliency,SOM,automatic initialisation,clustering,dominant colours,image segmentation | Computer vision,Data space,Pattern recognition,Segmentation,Salience (neuroscience),Computer science,Image segmentation,Unsupervised learning,Pixel,Artificial intelligence,Statistical classification,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 1 |
PageRank | References | Authors |
0.36 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dana E. Ilea | 1 | 81 | 3.71 |
| Paul F. Whelan | 2 | 561 | 39.95 |