OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Algorithm of Clustering for Color Images Segmentation
CONIELECOMP '05 Proceedings of the 15th International Conference on Electronics, Communications and Computers
SOM Segmentation of gray scale images for optical recognition
Pattern Recognition Letters
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm
IITSI '10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics
Document image segmentation using discriminative learning over connected components
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
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The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms-the classical K-Means, a modified Watershed segmentation as proposed by A. R. Kavitha et al., 2010 and their proposed Improved Clustering method normally used for gray scale image segmentation. The authors have analyzed the performance measure which affects the result of gray scale segmentation by considering three very important quality measures that is-Structural Content SC and Root Mean Square Error RMSE and Peak Signal to Noise Ratio PSNR as suggested by Jaskirat et al., 2012. Experimental result shows that, the proposed method gives remarkable consequence for the computed values of SC, RMSE and PSNR as compared to K-Means and modified Watershed segmentation. In addition to this, the end result of segmentation by means of the Proposed technique reduces the computational time as compared to the other two approaches irrespective of any input images.