Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 12
Abstract
This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.
Authors and Affiliations
Hind Shaaban, Farah Obaid, Ali Habib
Keywords
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- EP ID EP133189
- DOI 10.14569/IJACSA.2015.061224
- Views 101
- Downloads 0
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