Classification of the incorrect speech signals
ICCC'11 Proceedings of the 2011 international conference on Computers and computing
Two-dimensional clustering algorithms for image segmentation
WSEAS Transactions on Computers
Novel initialization scheme for Fuzzy C-Means algorithm on color image segmentation
Applied Soft Computing
Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation
Computers and Electrical Engineering
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Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Adaptive Fuzzy-K-means (AFKM) clustering for image segmentation which could be applied on general images and/or specific images (i.e., medical and microscopic images), captured using different consumer electronic products namely, for example, the common digital cameras and CCD cameras. The algorithm employs the concepts of fuzziness and belongingness to provide a better and more adaptive clustering process as compared to several conventional clustering algorithms. Both qualitative and quantitative analyses favour the proposed AFKM algorithm in terms of providing a better segmentation performance for various types of images and various number of segmented regions. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.