{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T05:54:34Z","timestamp":1721800474649},"reference-count":14,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,1,1]]},"abstract":"<p>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 \u2013 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 \u2013 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.<\/p>","DOI":"10.4018\/ijcvip.2013010102","type":"journal-article","created":{"date-parts":[[2013,7,3]],"date-time":"2013-07-03T18:50:58Z","timestamp":1372877458000},"page":"22-32","source":"Crossref","is-referenced-by-count":4,"title":["A Time Efficient Clustering Algorithm for Gray Scale Image Segmentation"],"prefix":"10.4018","volume":"3","author":[{"given":"Nihar Ranjan","family":"Nayak","sequence":"first","affiliation":[{"name":"Silicon Institute of Technology, Bhubaneswar, Odisha, India"}]},{"given":"Bikram Keshari","family":"Mishra","sequence":"additional","affiliation":[{"name":"Silicon Institute of Technology, Bhubaneswar, Odisha, India"}]},{"given":"Amiya Kumar","family":"Rath","sequence":"additional","affiliation":[{"name":"Dhaneswar Rath Institute of Engineering & Management Studies, Cuttack, Odisha, India"}]},{"given":"Sagarika","family":"Swain","sequence":"additional","affiliation":[{"name":"Koustav Institute of Self Domain, Bhubaneswar, Odisha, India"}]}],"member":"2432","reference":[{"key":"ijcvip.2013010102-0","doi-asserted-by":"crossref","unstructured":"Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. In Proceedings of the ACM SIGMOD\u201999 Conference on Management of Data (SIGMOD\u201999) (pp. 49-60).","DOI":"10.1145\/304181.304187"},{"key":"ijcvip.2013010102-1","unstructured":"Arthur, D., & Vassilvitskii, S. (2007). K-Means++: The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 1027-1035)."},{"key":"ijcvip.2013010102-2","doi-asserted-by":"crossref","unstructured":"Bukhari, S., Al Azawi, M., Shafait, F., & Breuel, T. (2010). Document image segmentation using discriminative learning over connected components. In Proceedings of the 8th IAPR International Workshop on Document Analysis Systems (pp. 183\u2013190). ACM.","DOI":"10.1145\/1815330.1815354"},{"key":"ijcvip.2013010102-3","doi-asserted-by":"crossref","unstructured":"Galaviz, P. M., Lopez, A. R., Garcia, M. J. S., & Torres, I. H. P. (2005). 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