Kernel-based fuzzy local information clustering algorithm self-integrating non-local information

Abstract

The application of fuzzy clustering in image segmentation is a research hotspot nowadays. Existing robust fuzzy clustering have some problems such as the inability to adaptively select spatial constraint parameters, the inability to accurately segment images corrupted by high noise, and the inability to achieve a balance between noise suppression and detail preservation. In the fuzzy clustering based on objective function optimization, the choice of distance measure is very important. Gaussian kernel function is defined by Euclidean distance and has been widely used in many fields such as pattern recognition, machine learning, etc. However, Euclidean distance in fuzzy clustering is very sensitive to outliers or noise, it is difficult to obtain satisfactory results for some special data sets, which will affect the performance of clustering algorithm. In this paper, a non local information self-integration optimization algorithm based on kernel-based fuzzy local information clustering algorithm is proposed. The algorithm uses the self-integration method on the basis of the local information of the image and introduces non-local information at the same time, which solves the common problems of current clustering algorithm. Firstly, the self-integrating method solves the problem of selecting spatial constraint parameters, and the algorithm continues self-learning and iteratively calculates the parameter values; secondly, the distance measure uses Gaussian kernel induced distance to further enhance the robustness against noise and the adaptability of processing data sets; Finally, the local and non-local information are integrated at the same time to achieve a segmentation effect, which can effectively suppress most of the noise and retain the details of original image. Experimental results show that the proposed algorithm is superior to existing state-of-the-art fuzzy clustering-related algorithm in the presence of high noise.