基于AFSA优化的FCM图像分割及其性能验证  

FCM Image Segmentation and Its Performance VerificationBased on AFSA Optimization

作　　者：刘美竹 安晶 Liu Meizhu;An Jing(School of Information and Electromechanical Engineering,Heilongjiang University of Business and Industry,Haerbin Heilongjiang 150025,China)

机构地区：[1]黑龙江工商学院信息与机电工程学院,黑龙江哈尔滨150025

出　　处：《山西电子技术》2024年第6期9-11,共3页Shanxi Electronic Technology

摘　　要：为了进一步提高图像分割能力,设计了一种自适应人工鱼群算法(AFSA),并用于FCM算法图像分割。以自标准图像库中图作为实验对象测试得到,与FCM算法相比,所提算法有更少的迭代次数,运作时间平均降低50%~55%。该算法的图像分割精准度相较于FCM算法提高7%左右、相较于FCM算法提高10%左右。所提基于AFSA算法达到FCM图像分割目的,在确保提高分割质量的前提下,便可在更短时间内完成图像分割工作,特别是对于比较繁杂的图像,可以充分展示出其优势。In order to further provide image segmentation capability,an adaptive artificial fish swarm algorithm(AFSA)is designed and applied to image segmentation by FCM algorithm.Compared with FCM algorithm,the proposed algorithm has fewer iterations and the operation time is reduced by 50%~55%on average.The image segmentation accuracy of this algorithm is about 7%higher than FCM algorithm and about 10%higher than FCM algorithm.The proposed AFSA algorithm achieves the purpose of FCM image segmentation,and can complete the image segmentation work in a shorter time under the premise of improving the segmentation quality,especially for more complicated images,which can fully demonstrate its advantages.

关 键 词：图像分割 模糊C均值 自适应人工鱼群算法 正确率 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]