CAJ | 학술논문

针对Fisher线性判决分析(FLDA)在图像识别应用中遇到的小样本问题,提出了两向二维最大子类散度差((2D)~2MCSD)鉴别分析的图像特征提取方法.首先找到每类数据的子类划分,再根据这些子类构造基于二维图像矩阵的子类类间和子类类内散布矩阵,最后用子类类间与子类类内散布之差作为鉴别准则求取投影矢量.该方法可以处理多模分布问题,从根本上避免了矩阵求逆和小样本问题,加快了特征抽取的速度,且同时对图像行和列进行压缩,克服了二维最大子类散度差(2DMCSD)鉴别分析和另一种形式的2DMCSD(Alternate 2DMCSD)的特征维数较大的问题.基于美国运动和静止目标获取与识别(MSTAR)公共数据库提供的实测数据的实验结果表明:本文方法的性能优于现有的子空间方法;与2DMCSD和Alter-nate 2DMCSD相比,可大大降低特征维数、提高识别性能.
침대Fisher선성판결분석(FLDA)재도상식별응용중우도적소양본문제,제출료량향이유최대자류산도차((2D)~2MCSD)감별분석적도상특정제취방법.수선조도매류수거적자류화분,재근거저사자류구조기우이유도상구진적자류류간화자류류내산포구진,최후용자류류간여자류류내산포지차작위감별준칙구취투영시량.해방법가이처리다모분포문제,종근본상피면료구진구역화소양본문제,가쾌료특정추취적속도,차동시대도상행화렬진행압축,극복료이유최대자류산도차(2DMCSD)감별분석화령일충형식적2DMCSD(Alternate 2DMCSD)적특정유수교대적문제.기우미국운동화정지목표획취여식별(MSTAR)공공수거고제공적실측수거적실험결과표명:본문방법적성능우우현유적자공간방법;여2DMCSD화Alter-nate 2DMCSD상비,가대대강저특정유수、제고식별성능.
To solve the small sample size (SSS) problem of Fisher linear discriminant analysis (FLDA) when it is applied to image recognition tasks, a novel image feature extraction technique is proposed which is called two-directional two-dimensional maximum clustering-based scatter difference ((2D)~2 MCSD) discriminant analysis. In this method, the possible clusters for each class are first found, and then the between-cluster and within-cluster scatter matrices are constructed from the 2D image matrices based on these clusters. Finally, projection vectors are sought by taking the difference of between-class scatter and within-class scatter as the discriminant criterion. Thus the method can not only deal with multimodal distribution problems but also avoid inverse matrix calculation and SSS problems, and increase the efficiency of feature extraction. Moreover, the (2D)~2 MCSD compresses image rows and columns simultaneously, thus overcoming the problem of too many features of 2DMCSD and Alternate 2DMCSD. Experiments on a moving and stationary target acquisition and recognition (MSTAR) public database demonstrate that the (2D)~2 MCSD is more efficient than some existing subspace methods. Furthermore, compared with 2DMCSD and Alternate 2DMCSD, (2D)~2 MCSD achieves higher recognition rates with much less memory requirements.