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