Color Image Segmentation using K-Means Clustering and Otsu’s Adaptive Thresholding
Vijay Jumb1, Mandar Sohani2, Avinash Shrivas3

1Vijay Jumb, Lecturer, Department of Computer, Xavier Institute of Engineering, Mumbai (Maharashtra), India.
2Prof. Mandar Sohani, Associate Professor, Department of Computer, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
3Prof. Avinash Shrivas, Assistant Professor, Department of Computer, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
Manuscript received on 13 February 2014 | Revised Manuscript received on 20 February 2014 | Manuscript Published on 28 February 2014 | PP: 72-76 | Volume-3 Issue-9, February 2014 | Retrieval Number: I1495023914/14©BEIESP
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Abstract: In this paper, an approach for color image segmentation is presented. In this method foreground objects are distinguished clearly from the background. As the HSV color space is similar to the way human eyes perceive color, hence in this method, first RGB image is converted to HSV (Hue, Saturation, Value) color model and V (Value) channel is extracted, as Value corresponds directly to the concept of intensity/brightness in the color basics section. Next an Otsu’s multi-thresholding is applied on V channel to get the best thresholds from the image. The result of Otsu’s multi-thresholding may consist of over segmented regions, hence K-means clustering is applied to merge the over segmented regions. Finally background subtraction is done along with morphological processing. This proposed system is applied on Berkley segmentation database. The proposed method is compared with three different types of segmentation algorithms that ensure accuracy and quality of different types of color images. The experimental results are obtained using metrics such as PSNR and MSE, which proves the proposed algorithm, produces better results as compared to other algorithms.
Keywords: Color Image Segmentation, HSV Color Space, Otsu’s Multi-Thresholding, K-Means Clustering, Morphological Processing, PSNR And MSE.

Scope of the Article: Clustering