Title :
Clustering with a mixture of self-organizing maps
Author :
Wesolkowski, Slawo
Author_Institution :
Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada
fDate :
6/24/1905 12:00:00 AM
Abstract :
In clustering, usually a single point or vector is used as a cluster prototype. The idea of clustering about principal curves has been recently introduced. Principal curves are functions that can characterize a set of nonlinear data. One way to create a principal curve is to apply a one-dimensional self-organizing map to the multidimensional data. In this paper, the mixture of self-organizing maps algorithm is presented. Results with respect to a color image segmentation task are shown and discussed
Keywords :
computer vision; image colour analysis; image segmentation; pattern clustering; self-organising feature maps; unsupervised learning; clustering; color image; computer vision; image segmentation; learning process; principal curves; self-organizing maps; Clustering algorithms; Color; Design engineering; Euclidean distance; Humans; Image segmentation; Multidimensional systems; Prototypes; Self organizing feature maps; Systems engineering and theory;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007511
