采用混合策略联合优化的模糊C-均值聚类信息熵点云简化算法

黄鹤; 黄佳慧; 刘国权; 王会峰; 高涛

doi:10.7652/xjtuxb202407020

您当前的位置：

首页 >

文章列表页 >

采用混合策略联合优化的模糊C-均值聚类信息熵点云简化算法

—————————————————————— | 更新时间：2025-07-28

- 采用混合策略联合优化的模糊C-均值聚类信息熵点云简化算法
- Fuzzy C-means Clustering Information Entropy Point Cloud Simplification Using Mixed Strategy Joint Optimization
- 西安交通大学学报 2024年58卷第7期页码：214-226
- 作者机构：
  
  1. 长安大学西安市智慧高速公路信息融合与控制重点实验室,西安,710064
  2. 长安大学电子与控制工程学院,西安,710064
  3. 长安大学数据科学与人工智能研究院,西安,710064
- 作者简介：
  
  : 2023-10-05。作者简介: 黄鹤(1979—),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(62341301)
- 基金信息：
  
  : 2023-10-05。作者简介: 黄鹤(1979—),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(62341301);西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金资助项目(300102323502);中央高校基本科研业务费资助项目(300102324501)
- DOI：10.7652/xjtuxb202407020
  中图分类号： U666.1
- 网络首发：2024-07-10，
  
  纸质出版：2024
- 稿件说明：
移动端阅览
黄鹤, 黄佳慧, 刘国权, 等. 采用混合策略联合优化的模糊C-均值聚类信息熵点云简化算法[J]. 西安交通大学学报, 2024,58(7):214-226.

HUANG He, HUANG Jiahui, LIU Guoquan, et al. Fuzzy C-means Clustering Information Entropy Point Cloud Simplification Using Mixed Strategy Joint Optimization[J]. 2024, 58(7): 214-226.
黄鹤, 黄佳慧, 刘国权, 等. 采用混合策略联合优化的模糊C-均值聚类信息熵点云简化算法[J]. 西安交通大学学报, 2024,58(7):214-226. DOI： 10.7652/xjtuxb202407020.

HUANG He, HUANG Jiahui, LIU Guoquan, et al. Fuzzy C-means Clustering Information Entropy Point Cloud Simplification Using Mixed Strategy Joint Optimization[J]. 2024, 58(7): 214-226. DOI： 10.7652/xjtuxb202407020.

摘要

针对传统聚类算法处理点云简化问题时精度低、耗时长且易丢失特征信息等问题

提出了一种基于动态精英自适应混合策略的鹈鹕算法(DEAMPOA)与加权熵法联合优化的模糊C-均值聚类(FCM)信息熵点云简化算法。采用动态自适应种群混合策略

同时融合了精英反向化思路

显著提升了鹈鹕优化算法(POA)的收敛趋势和全局寻优能力

提高了寻找FCM最优聚类中心的成功率; 利用DEAMPOA结合加权熵法对FCM进行优化

提高鲁棒性的同时增强了搜索精度

得到较好的聚类结果; 在8种UCI标准数据集上与4种算法对比进行聚类性能评估实验

验证了所提方法综合性能优越; 将所提方法与信息熵融合

并应用在三维点云KITTI数据集简化中。实验结果表明:与包围框简化法、随机采样简化法和特征选择简化法对比

所提方法全局误差简化前后点集之间平均欧式距离(MED)指标分别降低了2.25%、6.93%、5.74%

点云简化效果最优且运行速度满足要求。

Abstract

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 dataset 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.

关键词

Keywords

references

赵佳琦, 周勇, 何欣, 等. 基于深度学习的点云分割研究进展分析 [J]. 电子与信息学报, 2022, 44(12): 4426-4440.

ZHAO Jiaqi, ZHOU Yong, HE Xin, et al. Research progress analysis of point cloud segmentation based on deep learning [J]. Journal of Electronics & Information Technology, 2022, 44(12): 4426-4440.

王子洋, 李琼琼, 张子蕴, 等. 应用于无人驾驶车辆的点云聚类算法研究进展 [J]. 世界科技研究与发展, 2021, 43(3): 274-285.

WANG Ziyang, LI Qiongqiong, ZHANG Ziyun, et al. Research progress of unmanned vehicle point cloud clustering algorithm [J]. World Sci-Tech RD, 2021, 43(3): 274-285.

鲁斌, 范晓明. 基于改进自适应k均值聚类的三维点云骨架提取的研究 [J]. 自动化学报, 2022, 48(8): 1994-2006.

LU Bin, FAN Xiaoming. Research on 3D point cloud skeleton extraction based on improved adaptive k-means clustering [J]. Acta Automatica Sinica, 2022, 48(8): 1994-2006.

CHENG Fang, NIU Guofeng, ZHANG Zhizhong, et al. Improved CNN-based indoor localization by using RGB images and DBSCAN algorithm [J]. Sensors, 2022, 22(23): 9531.

SHI Yuqing. Application of FCM clustering algorithm in digital library management system [J]. Electronics, 2022, 11(23): 3916.

黄鹤, 李昕芮, 吴琨, 等. 引入改进飞蛾扑火的K均值交叉迭代聚类算法 [J]. 西安交通大学学报, 2020, 54(9): 32-39.

HUANG He, LI Xinrui, WU Kun, et al. Hybrid iterative K-means clustering with improved Moth-Flame optimization [J]. Journal of Xi'an Jiaotong University, 2020, 54(9): 32-39.

CHEN Yewang, ZHOU Lida, BOUGUILA N, et al. BLOCK-DBSCAN: fast clustering for large scale data [J]. Pattern Recognition, 2021, 109: 107624.

YANG Yang, LI Ming, MA Xie. A point cloud simplification method based on modified fuzzy C-means clustering algorithm with feature information reserved [J]. Mathematical Problems in Engineering, 2020, 2020: 5713137.

张佳琦, 王建民. 应用改进狼群算法优化模糊聚类实现点云数据的区域分割 [J]. 科学技术与工程, 2023, 23(30): 13002-13013.

ZHANG Jiaqi, WANG Jianmin. Region segmentation of point cloud data based on improved wolf pack algorithm optimization fuzzy clustering [J]. Science Technology and Engineering, 2023, 23(30): 13002-13013.

TROJOVSKY' P, DEHGHANI M. Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications [J]. Sensors, 2022, 22(3): 855.

张建华, 刘艺琳, 郭启迪, 等. 融合改进FCM与PFS的知识供需匹配 [J]. 计算机工程与设计, 2023, 44(1): 99-107.

ZHANG Jianhua, LIU Yilin, GUO Qidi, et al. Knowledge supply and demand matching based on improved FCM and PFS [J]. Computer Engineering and Design, 2023, 44(1): 99-107.

宋燕, 李元昊, 李明. 融合稀疏自表示和残差驱动的自适应模糊C均值聚类 [J]. 控制与决策, 2024, 39(4): 1333-1341.

SONG Yan, LI Yuanhao, LI Ming. Sparse self-representation incorporated and residual driven adaptive fuzzy C-means clustering [J]. Control and Decision, 2024, 39(4): 1333-1341.

吴辰文, 王莎莎, 曹雪同. 结合柯西分布和蚁狮算法改进的模糊聚类算法 [J]. 计算机工程与应用, 2023, 59(17): 91-98.

WU Chenwen, WANG Shasha, CAO Xuetong. Fuzzy clustering algorithm combined with Cauchy distribution and ant lion algorithm [J]. Computer Engineering and Applications, 2023, 59(17): 91-98.

EDWIN DHAS P, SANKARA GOMATHI B. A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data [J]. The Journal of Supercomputing, 2020, 76(8): 5679-5693.

黄鹤, 李文龙, 吴琨, 等. 动态自适应特征融合的MFOPA跟踪器 [J]. 电子学报, 2023, 51(5): 1350-1358.

HUANG He, LI Wenlong, WU Kun, et al. MFOPA tracker with dynamic adaptive feature fusion [J]. Acta Electronica Sinica, 2023, 51(5): 1350-1358.

XUE Jiankai, SHEN Bo. A novel swarm intelligence optimization approach: sparrow search algorithm [J]. Systems Science Control Engineering, 2020, 8(1): 22-34.

BEZDEK J C, EHRLICH R, FULL W. FCM: the fuzzy c-means clustering algorithm [J]. Computers Geosciences, 1984, 10(2/3): 191-203.

余庆, 胡尧. 基于改进FCM聚类算法的高速公路交通状态识别 [J]. 交通运输研究, 2021, 7(2): 47-54.

YU Qing, HU Yao. Identification on traffic state of expressway based on improved FCM clustering algo-rithm [J]. Transport Research, 2021, 7(2): 47-54.

刘张桥, 王成良, 焦晓军. 多核环境下的图像分割并行算法研究 [J]. 计算机工程, 2011, 37(15): 197-200.

LIU Zhangqiao, WANG Chengliang, JIAO Xiaojun. Research of parallel algorithm for image segmentation under multi-core environment [J]. Computer Engineering, 2011, 37(15): 197-200.

赵夫群, 李映萱. 基于点重要性判断的点云简化 [J]. 计算机系统应用, 2023, 32(9): 197-202.

ZHAO Fuqun, LI Yingxuan. Point cloud simplification based on point importance judgment [J]. Computer Systems Applications, 2023, 32(9): 197-202.

HE Siwei, LIU Baolong. Review of bounding box algorithm based on 3D point cloud [J]. International Journal of Advanced Network Monitoring and Controls, 2021, 6(1): 18-23.

孔利, 王延存, 周茂伦, 等. 基于随机抽样与特征值法的点云平面稳健拟合方法 [J]. 测绘与空间地理信息, 2019, 42(3): 43-46.

KONG Li, WANG Yancun, ZHOU Maolun, et al. A robust point cloud planar fitting method based on random sampling and eigenvalue [J]. Geomatics Spatial Information Technology, 2019, 42(3): 43-46.

MAHDAOUI A, SBAI E H. 3D point cloud simplification based on k-nearest neighbor and clustering [J]. Advances in Multimedia, 2020, 2020: 8825205.

THARWAT A. Parameter investigation of support vector machine classifier with kernel functions [J]. Knowledge and Information Systems, 2019, 61(3): 1269-1302.

陈辉, 黄晓铭, 刘万泉. 基于动态网格k邻域搜索的激光点云精简算法 [J]. 控制与决策, 2020, 35(12): 2986-2992.

CHEN Hui, HUANG Xiaoming, LIU Wanquan. Laser point cloud simplification algorithm based on dynamic grid k-nearest neighbors searching [J]. Control and Decision, 2020, 35(12): 2986-2992.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向移动节点不确定性特征的自组网路由协议

特征保持的点云精简技术研究

利用膜粒子群优化和信息熵的医学文本特征选择

主动配电网多维度静态安全评估

一种采用相空间重构的多源数据融合方法