Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation
Science Journal of Circuits, Systems and Signal Processing
Volume 3, Issue 6-1, December 2014, Pages: 1-5
Received: Sep. 17, 2014; Accepted: Sep. 22, 2014; Published: Oct. 20, 2014
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Authors
Arunkumar Rajendran, M E Communication Systems, Karpagam College of Engineering, Coimbatore, India
Thamarai Muthusamy, ECE Department, Karpagam College of Engineering, Coimbatore, India
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Abstract
In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.
Keywords
Fuzzy C Means, Image Segmentation, Unsupervised Learning, AUFCM and DCTS
To cite this article
Arunkumar Rajendran, Thamarai Muthusamy, Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation, Science Journal of Circuits, Systems and Signal Processing. Special Issue:Computational Intelligence in Digital Image Processing. Vol. 3, No. 6-1, 2014, pp. 1-5. doi: 10.11648/j.cssp.s.2014030601.11
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