基于密度特征与KNN算法的最优特征维数选择  

Optimal feature dimension selection based on density feature and KNN algorithm

作　　者：孙国栋[1] 梅术正 汤汉兵 周振 SUN Guodong;MEI Shuzheng;TANG Hanbing;ZHOU Zhen(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学机械工程学院,湖北武汉430068

出　　处：《现代电子技术》2018年第16期80-83,共4页Modern Electronics Technique

基　　金：国家自然科学基金项目资助(51675166);国家自然科学基金项目资助(51205115)~~

摘　　要：为了保证基于同步触发双相机的仪表复杂字符识别中误识率为0,采用K最近邻算法对仪表字符特征进行训练分类,结合字符自身特点,提出最优特征提取与高宽维度选择方法,并设计实验获取1~4 096维密度特征的误识率与运行时间。实验结果表明,图像的密度特征总维度在230~260,高宽维度比接近1.4时,误识率为0的概率最大。该规律对采用KNN算法进行分类识别时最优密度特征维数选择具有一定指导意义。The K.Nearest Neighbor(KNN)algorithm is adopted to train and classify the character features of the instrument to guarantee the zero error recognition rate during the complex character recognition of the instrument based on the double cameras with synchronous trigger.In combination with the features of the character,a method of extracting the optimum feature and selecting the width and height dimensions is proposed.An experiment was designed to obtain the error recognition rate and running time of 1~4096 dimensions density features.The experimental results show that when the total number of dimensions of the image density feature is 230~260 and the dimension ratio of height to width is close to 1.4,the probability of zero error recognition rate reaches the maximum.This rule has a certain guiding significance to the selection of the optimal density feature dimension when the KNN algorithm is used for classification and recognition.

关 键 词：复杂仪表 特征维数 误识率 KNN算法 密度特征 最优特征 

分 类 号：TN911.73-34[电子电信—通信与信息系统] TP23[电子电信—信息与通信工程]