A New K-mean Color Image Segmentation with Cosine Distance for Satellite Images
Modh Jigar S1, Shah Brijesh2, Shah Satish k3
1Modh Jigar S, PG student, V.T. Patel Department of Electronics and Communication Engineering.
2Shah Brijesh, Associate Professor, Associate Professor, C S. Patel Institute of Technology, CHARUSAT, Changa, Gujarat, India.
3Shah Satish k., Professor M. S. University, Baroda, Gujarat, India.
Manuscript received on may 27, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 27-30 | Volume-1 Issue-5, June 2012 | Retrieval Number: E0389051512/2012©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: This paper represents unsupervised method of k-means segmentation which is new adaptive technique of color-texture segmentation. With the progress in satellite images, the image segmentation technique for generating and updating geographical information are become more and more important. This algorithm first enhance the image then applying clustering based k-means segmentation technique ,using L*a*b color space and using cosine distance matrices instead of sqeuclidean distance. With this it is possible to reduce computational time and calculation for every pixel in the image .Although colors are not frequently used in image segmentation; it gives high discriminative power to the regions present in image.
Keywords: K-means segmentation, cosine distance ,Euclidean distance,