DocumentCode :
147155
Title :
Automatic centroids selection in K-means clustering based image segmentation
Author :
Pugazhenthi, A. ; Singhai, Jyoti
Author_Institution :
Electron. & Commun. Eng. Dept, G Pulla Reddy Eng. Coll., Kurnool, India
fYear :
2014
fDate :
3-5 April 2014
Firstpage :
1279
Lastpage :
1284
Abstract :
This paper proposes a K-means clustering based image segmentation algorithm which select the centroids automatically. It eliminates the limitations associated with K-means clustering such as selection of initial centroids and dead centers. As image histogram is one of the best ways to represent the distribution of image gray levels, the proposed approach selects centroids as the gray levels corresponding to the peaks of the image histogram. With these initial centroids, K-means clustering is performed. The result is post processed by some morphological operations. The proposed algorithm uniformly segments the regions of interest over randomly selected centroids. The performance of proposed algorithm and random centroids selection is compared with some validity parameters like SSIM, MSE, PSNR, IF, SC and CC. Comparison with the existing algorithms confirms the improvement in qualitative parameters. The tool used in this work is MATLAB R2012a.
Keywords :
image segmentation; pattern clustering; K-means clustering based image segmentation; MATLAB R2012a; automatic centroids selection; dead centers; image gray levels; image histogram; Algorithm design and analysis; Clustering algorithms; Colored noise; Image color analysis; Image segmentation; Noise measurement; PSNR; Clustering; Image Segmentation; K means Clustering; Segmentation Quality Measure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications and Signal Processing (ICCSP), 2014 International Conference on
Conference_Location :
Melmaruvathur
Print_ISBN :
978-1-4799-3357-0
Type :
conf
DOI :
10.1109/ICCSP.2014.6950057
Filename :
6950057
Link To Document :
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