Optimized Fuzzy C-means Clustering Algorithm In Buyi Costume Image Segmentation

The costumes of ethnic minorities have always been an indispensable part of the Chinese culture.The costumes of ethnic minorities in China have many characteristics,and the patterns on the costumes attract attention.Each pattern has its special significance.From these patterns,we can understand the culture of each nation,the implied meaning conveyed by the symbols of national culture,and also grasp the spiritual connotation of Chinese national culture.It is of academic significance and cultural protection value to analyze and study minority costume patterns through image segmentation.Image segmentation is an important branch in the field of modern computer technology and meta-universe block chain.This theory has been successfully applied to many fields,such as medicine,astronomy,intelligent transportation and so on,but it is relatively seldom applied in the image segmentation of ethnic clothing.The main research content of this paper is as follows:(1)Fuzzy c-means Buyi clothing image segmentation algorithm based on morphological reconstruction and membership filtering.Because the improved algorithm of fuzzy c-means based on morphological reconstruction and membership filtering uses morphological reconstruction to smooth the image,enhance denoising and preserve image details,it eliminates the problem that the previous fuzzy c-means clustering algorithm is not robust to noise.At the same time,the algorithm uses the membership filtering to modify the membership partition matrix,avoids calculating the distance between the pixels in the local space neighborhood and the cluster center,effectively reduces the computational complexity,and makes the algorithm simple and fast.This paper takes the noise image and Buyi clothing image as the segmentation object,verifies the segmentation accuracy and speed of the algorithm in the MATLAB environment,and compares it with the more popular fuzzy c-means clustering algorithm.The experimental results show that the algorithm has high segmentation accuracy and rapidity.(2)An improved fast robust double indices fuzzy c-means clustering algorithm for Buyi clothing image segmentation is proposed.Based on the fuzzy c-means algorithm based on morphological reconstruction and membership filtering,the algorithm extends the power index of constraints and introduces the power index r into constraints.The performance of the algorithm is determined by the fuzzy index m and the power index r of constraints,which effectively expands the value range of m and makes the algorithm more flexible and universal.Similarly,in the MATLAB environment,the noise image and Buyi clothing image are used as segmentation objects to verify the segmentation accuracy and speed of the proposed fast and robust double indices fuzzy c-means clustering algorithm,and the algorithm is compared with other comparison algorithms.Experimental results show that the algorithm can get good segmentation results with low computational cost and high segmentation accuracy.