Recently,sparse representation classification(SRC)and fisher discrimination dictionary learning(FDDL)methods have emerged as important methods for vehicle classification.In this paper,inspired by recent breakthroughs ...Recently,sparse representation classification(SRC)and fisher discrimination dictionary learning(FDDL)methods have emerged as important methods for vehicle classification.In this paper,inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection,we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors.To improve the classification accuracy in complex scenes,we develop a new method,called multi-task joint sparse representation classification based on fisher discrimination dictionary learning,for vehicle classification.In our proposed method,the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients(MFCC).Moreover,we extend our model to handle sparse environmental noise.We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.展开更多
近年来,稀疏表示分类(Sparse Representation Based Classification,SRC)方法在人脸识别中受到越来越多的关注。原始SRC方法使用所有的训练样本组成字典矩阵,当训练样本比较多时,稀疏系数的求解会变得非常耗时。为了解决这一问题,提出...近年来,稀疏表示分类(Sparse Representation Based Classification,SRC)方法在人脸识别中受到越来越多的关注。原始SRC方法使用所有的训练样本组成字典矩阵,当训练样本比较多时,稀疏系数的求解会变得非常耗时。为了解决这一问题,提出一种新的局部稀疏表示分类(Local SRC,LSRC)方法。该方法针对每个测试样本,根据测试样本和训练样本稀疏系数之间的相似性来选择部分训练样本,由这些训练样本组成字典,然后在这个字典上对测试样本进行稀疏分解。该方法性能相比于原始LSRC方法更稳定。在ORL、Yale和AR人脸库上的实验结果表明,该方法的效果优于SRC和LSRC。展开更多
基金This work was supported by National Natural Science Foundation of China(NSFC)under Grant No.61771299,No.61771322,No.61375015,No.61301027.
文摘Recently,sparse representation classification(SRC)and fisher discrimination dictionary learning(FDDL)methods have emerged as important methods for vehicle classification.In this paper,inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection,we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors.To improve the classification accuracy in complex scenes,we develop a new method,called multi-task joint sparse representation classification based on fisher discrimination dictionary learning,for vehicle classification.In our proposed method,the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients(MFCC).Moreover,we extend our model to handle sparse environmental noise.We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.
文摘近年来,稀疏表示分类(Sparse Representation Based Classification,SRC)方法在人脸识别中受到越来越多的关注。原始SRC方法使用所有的训练样本组成字典矩阵,当训练样本比较多时,稀疏系数的求解会变得非常耗时。为了解决这一问题,提出一种新的局部稀疏表示分类(Local SRC,LSRC)方法。该方法针对每个测试样本,根据测试样本和训练样本稀疏系数之间的相似性来选择部分训练样本,由这些训练样本组成字典,然后在这个字典上对测试样本进行稀疏分解。该方法性能相比于原始LSRC方法更稳定。在ORL、Yale和AR人脸库上的实验结果表明,该方法的效果优于SRC和LSRC。
