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2007 6th International Conference on Computer Information Systems and Industrial Management Applications
Standard and Genetic k-means Clustering Techniques in Image Segmentation
Elk, Poland
June 28-June 30
ISBN: 0-7695-2894-5
ASCII Text
x 
 
Dariusz Malyszko, Slawomir T. Wierzchon, "Standard and Genetic k-means Clustering Techniques in Image Segmentation," Computer Information Systems and Industrial Management Applications, International Conference on, pp. 299-304, 2007 6th International Conference on Computer Information Systems and Industrial Management Applications, 2007.
 
 
BibTex
x
 
@article{ 10.1109/CISIM.2007.63,
author = {Dariusz Malyszko and Slawomir T. Wierzchon},
title = {Standard and Genetic k-means Clustering Techniques in Image Segmentation},
journal ={Computer Information Systems and Industrial Management Applications, International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2894-5},
pages = {299-304},
doi = {http://doi.ieeecomputersociety.org/10.1109/CISIM.2007.63},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Information Systems and Industrial Management Applications, International Conference on
TI - Standard and Genetic k-means Clustering Techniques in Image Segmentation
SN - 0-7695-2894-5
SP299
EP304
A1 - Dariusz Malyszko,
A1 - Slawomir T. Wierzchon,
PY - 2007
KW - null
VL - 0
JA - Computer Information Systems and Industrial Management Applications, International Conference on
ER -
 
Dariusz Malyszko, Technical University of Bialystok, Poland
Slawomir T. Wierzchon, University of Gdansk, Poland; Polish Academy of Sciences, Poland
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.
Citation:
Dariusz Malyszko, Slawomir T. Wierzchon, "Standard and Genetic k-means Clustering Techniques in Image Segmentation," cisim, pp.299-304, 2007 6th International Conference on Computer Information Systems and Industrial Management Applications, 2007
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