摘要
针对F范数对离群数据较为敏感,而L1范数能降低离群数据的影响,但无法有效控制重构误差的问题,本文将L1范数与F范数同时作为目标函数的距离度量方式,提出了二维主成分分析(two-dimensional principle component analysis,2DPCA)联合算法2DPCA-F-L1,并给出了其非贪婪求解方法。该算法确保了对图像的分类能力,同时也降低了图像重构时的平均重构误差。本文将提出的2DPCA-F-L1算法在应用于水下生物图像识别时,可以抑制水下光学影像存在的噪声干扰。实验证明,该算法能够精确地识别水下生物的种类,并且在图像重构时相较于其他主成分分析(principle component analysis,PCA)算法具有更优的鲁棒性。
F-norm is sensitive to outlier data,while L1-norm can significantly reduce the sensitivity and cannot effectively control reconstruction errors.To tackle the problem,we take both F-norm and L1-norm as the distance metric of the objective function,and propose a joint-norm two-dimensional principal component analysis(2 DPCA)algorithm called 2 DPCA-F-L1,and give its non-greedy solution.This algorithm not only ensure the ability of image classification,but also decrease the average reconstruction error in image reconstruction.When applied to underwater biometric image recognition,the proposed 2 DPCA-F-L1 suppresses the noise interference in underwater optical images.Experiments show that the 2 DPCA-F-L1 algorithm can accurately recognize the species of underwater creatures,and has better robustness than other principal component analysis(PCA)algorithms in image reconstruction experiments.
作者
张浣星
王肖锋
武刚
ZHANG Huanxing;WANG Xiaofeng;WU Gang(Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control,School of Mechanical Engineering,Tianjin University of Technology,Tianjin 300384,China;National Demonstration Center for Experimental Mechanical and Electrical Engineering Education,Tianjin University of Technology,Tianjin 300384,China)
出处
《光电子．激光》
CAS
CSCD
北大核心
2022年第10期1067-1074,共8页
Journal of Optoelectronics·Laser
基金
国家重点研发计划(2018AA0103004)
天津市科技计划重大专项(20YFZCGX00550)资助项目
关键词
主成分分析(PCA)
生物识别
图像识别
鲁棒性
联合范数
principal component analysis(PCA)
biometric recognition
image recognition
robustness
joint norm
