Fuzzy C-means Clustering Information Entropy Point Cloud Simplification Using Mixed Strategy Joint Optimization

To solve the problems of low accuracy, long time consumption, and easy loss of feature information of the traditional clustering algorithm in conducting point cloud simplification, a point cloud simplification method for FCM information entropy jointly optimized by DEAMPOA and weighted entropy method is proposed. Firstly, a dynamic adaptive population mixing strategy is proposed, which integrates the elite reverse idea. This strategy significantly improves the convergence trend and global optimization ability of POA and increases the success rate of finding the optimal clustering center of FCM. Secondly, the optimization of FCM using DEAMPOA combined with weighted entropy method improves the robustness while enhancing the search accuracy, yielding better clustering results. Thirdly, clustering performance evaluation experiments are carried out through comparison with four comparison algorithms in eight UCI standard datasets, which verifies that the proposed method has superior comprehensive performance. Finally, the proposed method is fused with information entropy and applied to the KITTI point cloud datasct simplification. The experimental results show that the global error MED index of the proposed method is reduced by 2. 25%, 6. 93%, and 5. 74% respectively compared with that of the bounding frame simplification method, random sampling simplification method, and feature selection simplification method. Furthermore, this method generates the optimal point cloud simplification effect and the running speed meets the requirements. © 2024 Xi'an Jiaotong University. All rights reserved.