基于RGB空间的非经典K最近邻算法应用研究  

APPLICATION RESEARCH OF NON-CLASSICAL K-NEAREST NEIGHBOR ALGORITHM BASED ON RGB SPACE

作　　者：丁茹茗 徐晓光[1,2] 刘瑞 郝旭耀[3] DING Ru-ming;XU Xiao-guang;LIU Rui;HAO Xu-yao(Key Laboratory ofAdvanced Perception and Intelligent Control of High-end Equipment,Ministry of EducationAnhui Polytechnic University,Wuhu,Anhui 241000,China;School of Electrical Engineering,Anhui Polytechnic University,Wuhu,Anhui 241000,China;Anhui Zuobiao Intelligent Technology Company,Wuhu,Anhui 241000,China)

机构地区：[1]安徽工程大学高端装备先进感知与智能控制教育部重点实验室,安徽芜湖241000 [2]安徽工程大学电气工程学院,安徽芜湖241000 [3]安徽佐标智能科技有限公司,安徽芜湖241000

出　　处：《井冈山大学学报（自然科学版）》2023年第3期70-75,90,共7页Journal of Jinggangshan University (Natural Science)

基　　金：国家自然科学基金项目(61903002);安徽省高校自然科学研究项目(KJ2020A0350);芜湖市科技计划项目(2020cg12);安徽工程大学-鸠江区产业协同创新专项基金项目(2022cyxtb9)。

摘　　要：为迅速、准确、无过多人工干预的进行图像分割,提出了一种K最近邻算聚类方法并将其应用于图像处理。与经典K最近邻算法在样本库中寻找最近邻点不同,该算法在待分割图像的RGB空间中寻找每一个像素点的K个最近邻点,参考所有像素点同最近邻点之间的平均距离,引入聚类阈值并对像素点的归属进行判断。对火焰图像的分割实验结果表明,在分割精度相接近的情况下,该算法的分割速度要快于其它几种常见算法。In order to segment the images quickly,accurately and without too much manual intervention,one K nearest neighbor clustering method was proposed and applied in image processing.Being different from the classical K-nearest neighbor algorithm,by which the nearest neighbor points were found in the sample set,the K nearest neighbors of each pixel in the RGB space of the image to be processed were found by the proposed algorithm.Referring to the average distance between all pixels and their K nearest neighbors,a clustering threshold associated with this distance was introduced and the attribution of each pixel was judged.The experimental results of flame image segmentation showed that when the segmentation accuracy was close,the segmentation speed of this algorithm was faster than that of other common algorithms.

关 键 词：图像分割 K-最近邻 聚类分析 火焰识别 颜色空间 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]