DocumentCode :
3625490
Title :
Standard and Genetic k-means Clustering Techniques in Image Segmentation
Author :
Dariusz Malyszko;Slawomir T. Wierzchon
Author_Institution :
Technical University of Bialystok, Poland
fYear :
2007
fDate :
6/1/2007 12:00:00 AM
Firstpage :
299
Lastpage :
304
Abstract :
Clustering or data grouping is a key initial procedure in image processing. This paper deals with the application of standard and genetic k-means clustering algorithms in the area of image segmentation. In order to assess and compare both versions of k-means algorithm and its variants, appropriate procedures and software have been designed and implemented. Experimental results point that genetically optimized k-means algorithms proved their usefulness in the area of image analysis, yielding comparable and even better segmentation results.
Keywords :
"Image segmentation","Clustering algorithms","Genetic algorithms","Partitioning algorithms","Application software","Algorithm design and analysis","Image analysis","Robustness","Data analysis","Iterative algorithms"
Publisher :
ieee
Conference_Titel :
Computer Information Systems and Industrial Management Applications, 2007. CISIM ´07. 6th International Conference on
Print_ISBN :
0-7695-2894-5
Type :
conf
DOI :
10.1109/CISIM.2007.63
Filename :
4273538
Link To Document :
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