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Image Segmentation using Gaussian Mixture Adaptive Fuzzy C-mean Clustering




Vol. 13  No. 10  pp. 114-118


This paper presents a new approach for image segmentation by applying Gaussian mixture. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies fuzzy C-means clustering to the image segmentation after optimized by Gaussian Algorithm. The algorithm considers the centroid placement which should be located as far as possible from each other to with stand against the pressure distribution, as identical to the number of centroids amongst the data distribution. This algorithm is able to optimize the fuzzy C-means clustering for image segmentation in aspects of precision and computation time. It designates the initial centroids’ positions by calculating the probability distance metric between each data point and all previous centroids, and then selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum probability distance metric. This paper evaluates the proposed approach for image segmentation by comparing with fuzzy c-mean clustering and Gaussian Mixture Model algorithm and involving RGB, HSV, HSL and CIELAB color spaces. The experimental results clarify the effectiveness of our approach to improve the segmentation quality in aspects of precision and computational time.


Gaussian mixture model, fuzzy c-mean clustering ,image segmentation