Benner
فرح عباس ساري ( مدرس )
كلية علوم الحاسوب والرياضيات - الحاسوب
[email protected]
 
 
 
Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier
تحميل
بحث النوع:
علوم التخصص العام:
Hind Shaaban اسم الناشر:
Ali Alramahi,Farah Abbas اسماء المساعدين:
International Journal of Advanced Computer Science and Applications, الجهة الناشرة:
Vol. 6, No. 12, 2015 176  
2016 سنة النشر:

الخلاصة

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.