Research On Fuzzy-Clustering-Based Method Of Image Segmentation

Image segmentation is the process of detecting objects or interesting areas from input image, and it is an important step in object detection and recognition. Fuzzy clustering is an important branch of fuzzy set theory, and is widely applied in image segmentation. In this dissertation, the application of fuzzy clustering in image segmentation is studied. The main work of this dissertation is summarized as follows:1) The dissertation systematically introduces the fundamental knowledge of fuzzy set theory, fuzzy clustering algorithms and its application in image segmentation— fast fuzzy c-means clustering algorithm based on histogram.2) A fuzzy clustering algorithm is given which combines simulated annealing (SA) and fuzzy c-means (FCM) clustering. This algorithm can reduce the influence of selection of the initial clustering centers value and the membership matrix' elements on the algorithm convergence. Based on choosing reasonable cooling schedule, the objection function for SA is set up according to FCM clustering, and the image segmentation algorithm based on SA and FCM clustering is implemented.3) Based on kernel method, an image segmentation algorithm of fuzzy kernel c-means is proposed. By using Mercer kernel to map the samples from original space to a high-dimension feature space, we can get good results of clustering.