Experimental Study of Image Segmentation Using K-Means Clustering Algorithm with Image Quality Metrics
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 10
Abstract
Image Segmentation has been considered as one of the significant tool for image analysis. The main objective of image analysis is to understand the component of the image and extract useful information using image features. Over the past two decades, there have been various studies on a variety of well-known probabilistic approach for image segmentation. In this paper we have describe some probabilistic approach towards image segmentation, along with an experimental study of image segmentation using K-Means clustering Algorithm. Initialization of the cluster k is done by obtaining the image region using hierarchical clustering. Image Quality Metrics such Average Difference(AD), Maximum Difference(MD),Image Fidelity(IF),Peak Mean Square Error(PMSE),Signal to Noise Ratio (PSNR) are evaluated to observe the performance and presented. The summary of the segmented model and their most distinguishing features is then presented in the table at the end of this paper.
Authors and Affiliations
Shameem Fatima, Dr. M. Seshashayee
Keywords
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