A Novel Approach Towards Clustering Based Image Segmentation
Dibya Jyoti Bora1, Anil Kumar Gupta2

1Mr. Dibya Jyoti Bora, Currently Teaching PG Students in the Department of Computer Science & Applications, Barkatullah University, Bhopal, India.
2Dr. Anil Kumar Gupta, HOD of the Department of Computer Science & Applications, Barkatullah University, Bhopal, India.
Manuscript received on September 15, 2014. | Revised Manuscript received on September 22, 2014. | Manuscript published on September 25, 2014. | PP:6-10 | Volume-2 Issue-11, September 2014. | Retrieval Number: K08060921114/2014©BEIESP

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© The Authors. Published By: 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: In computer vision, image segmentation is always selected as a major research topic by researchers. Due to its vital rule in image processing, there always arises the need of a better image segmentation method. Clustering is an unsupervised study with its application in almost every field of science and engineering. Many researchers used clustering in image segmentation process. But still there requires improvement of such approaches. In this paper, a novel approach for clustering based image segmentation is proposed. Here, we give importance on color space and choose l*a*b* for this task. The famous hard clustering algorithm K-means is used, but as its performance is dependent on choosing a proper distance measure, so, we go for “cosine” distance measure. Then the segmented image is filtered with sobel filter. The filtered image is analyzed with marker watershed algorithm to have the final segmented result of our original image. The MSE and PSNR values are evaluated to observe the performance.
Keywords: Computer vision, Image processing, Color Image segmentation, K-Means, Watershed