基于Canopy聚类的噪声自适应模糊C-均值算法

iming at the problem that the spatial influence factors are easily misidentified by noise in the fuzzy C-means algorithm (WFLICM) for local spatial information, this paper proposed a fuzzy C-means clustering algorithm for image segmentation (NLWFLICM) based on local and non-local spatial information. It introduced the non-local spatial information into the fuzzy influencing factor of WFLICM algorithm, the weight of local and non-local information is adaptively set according to the noise level, and the fuzzy influence factors of the central point are re-marked. The experimental results show that the NLWFLICM algorithm is more robust and adaptive than the WFLICM algorithm, and improves the robustness of the WFLICM algorithm to a large extent, while preserving the image texture. In order to improve the clustering performance and convergence speed of the algorithm, combined with the advantages of Canopy algorithm for fast clustering of data, this paper proposes an improved algorithm for FCM image segmentation based on Canopy clustering and non-local spatial information (Canopy-NLWFLICM) before clustering algorithm. This can improve the convergence speed and image segmentation accuracy. Key words: clustering algorithm ; Canopy algorithm ; fuzzy C-means cluster; local and non-local spatial information