Research On Fuzzy Clustering Image Segmentation Algorithm Based On Adaptive Information Fusio

Image segmentation plays a basic role in image analysis and image understanding.The quality of image segmentation directly affects the subsequent image analysis and image understanding,therefore,the study of image segmentation technology has important research value and application significance.Among various kinds of image segmentation methods,Fuzzy C-Means algorithm(FCM)is widely used in image segmentation task for its simple and efficient characteristics,and has become a classic method used for image segmentation.But conventional fuzzy clustering methods always suffer rather poor results when dealing with images with noise or some artifacts.In numerous improvment of FCM,membership regularized fuzzy clustering methods apply an important prior that neighboring data points should possess similar memberships according to an affinity matrix.As result,they achieve good performance in many tasks.However,these clustering methods fail to take full advantage of image spatial information in their regularizations.Their performance in image segmentation problem is still not promising.In addition,for specific image tasks,it is difficult to choose the most suitable fuzzy clustering algorithm for image processing.In order to solve this problem,fuzzy clustering integration method emerges.In fuzzy cluster ensemble,membership vectors generated from different fuzzy clustering methods are merged into one vector as an input object.However,this kind of input object may lose detail information from original target image and may cause inaccurate edges in segmentation results.Based on the problems existing in the above two fuzzy clustering methods,the main research contents are as follows:(1)In this paper,we first focus on building a novel affinity matrix to store and present the image spatial information as the prior to help membership regularized fuzzy clustering methods get excellent segmentation results.To this end,the affinity value is calculated by the fusion of pixel and region level information to present the subtle relationship of two points in an image.In addition,to reduce the impact of image noise,we use fixed cluster centers in the iteration of algorithm,thus,the updating of membership values is only guided by the prior of fused information.Experimental results over synthetic and real image datasets demonstrate that the proposed method shows better segmentation results than state-of-the-art clustering methods.(2)By means of treating membership matrix as a new representation of original data,the membership vector of fuzzy cluster ensemble should be intuitively geometric consistent with the original target image.In this paper,by holding this view,we develop a geometric consistent fuzzy cluster ensemble model for spatial data,which involves a constraint between the membership and its reconstruction,to improve the clustering performance on image segmentation.In the proposed model,a pre-determined gradient-preserving weight is used in the membership reconstruction item to make the membership matrix be geometric consistent with the original target image.A semi-implicit optimization iterative algorithm is adopted to solve the proposed geometric consistent model.Experimental results demonstrate the effectiveness of proposed model in synthetic and real-world image segmentation problems over several state-of-the-art methods.