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|Title:||FUZZY C-MEAN TECHNIQUE FOR IMAGE SEGMENTATION|
|Keywords:||Fuzzy C-means;Effective Kernel Clustering;Multiple Kernel;Imagesegmentation|
|Publisher:||I I T ROORKEE|
|Abstract:||In this thesis, Fuzzy C-means technique is presented as basis for image segmentation process. The various aspects of working of Fuzzy C-means algorithms are highlighted and the sequential development of these algorithms is given. The advantages of kernel functions and their use in the process of image segmentation are specified. Fuzzy C-Means (FCM) is a prevalent soft-clustering technique. This clustering technique is widely used in the task of image segmentation because of its ease of execution and rapid convergence. By using kernel properties, the Kernel Fuzzy C-Means algorithm attempts to map the data with nonlinear relationships to appropriate higher dimensional spaces. In the new space, the data can be more easily separated or clustered. Kernel combination, or selection, is fundamental for effective kernel clustering. By incorporating multiple kernels and automatically adjusting the kernel weights, Multiple Kernel Fuzzy C-Mean (MKFC) method is more immune to ineffective kernels and their extraneous features. This makes the choice of kernels less crucial and the method more effective for image segmentation. Effective kernels and associated features tend to contribute more to the clustering and, therefore, improve results of image segmentation. The MKFCM algorithm provides us a new platform to blend different types of image information in imagesegmentation problems. In this report, the technique for weight optimization is presented whereby obviating the need for centre calculations for the clusters and improving the performance of Multiple Kernel Fuzzy C-Mean algorithm. Simulations on the segmentation of synthetic, medical image and other images demonstrate the flexibility and advantages of MKFCM based approaches for image segmentation. The values of various parameters involved in the MKFCM algorithm are studied and guidelines for value selection are suggested. The fuzzy c-mean technique for image segmentation is a robust, easy to realize and effective methodology. Apart from these advantages it offers a great benefit by providing a platform for information fusion.|
|Appears in Collections:||MASTERS' THESES (Electrical Engg)|
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