Image Segmentation with Modified K-Means Clustering Method
Pushpa .R. Suri1, Mahak2
1Assistant Professor Dr. Pushpa. R. Suri, Department of Computer Science and Application., Kurukshetra University, Kurukshetra (Haryana), India.
2Scholar Mahak, Department of Computer Science and Application., Kurukshetra University, Kurukshetra (Haryana), India.
Manuscript received on 18 June 2012 | Revised Manuscript received on 25 June 2012 | Manuscript published on 30 June 2012 | PP: 176-180 | Volume-1 Issue-2, June 2012 | Retrieval Number: B0234051212/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image segmentation is used to recognizing some objects or something that is more meaningful and easier to analyze In this paper we are focus on the the K means clustering for segmentation of the image. K-means clustering is the most widely used clustering algorithm to position the radial basis function (RBF) centres. Its simplicity and ability to perform on-line clustering may inspire this choice. However, k-means clustering algorithm can be sensitive to the initial centres and the search for the optimum centre locations may result in poor local minima. Many attempts have been made to minimise these problems In this paper two updating rules were suggested as alternatives or improvements to the standard adaptive k-means clustering algorithm. The updating methods are proposed to give better overall RBF network performance rather than good clustering performance. However, there is a strong correlation between good clustering and the performance of the RBF network. The sensitivity of the RBF network to the centre locations will also be studied.Thus we will test the modified K means different set of images.
Keywords: Image Segmentation, Anisotropic Diffusion ,Smoothing Filters, Contrast Enchancement.
Scope of the Article: Image Security