[1]周俊星,梁 路*.一种动态剪枝的协同稀疏表示方法研究及应用[J].江西师范大学学报(自然科学版),2020,(05):472-477.[doi:10.16357/j.cnki.issn1000-5862.2020.05.04]
 ZHOU Junxing,LIANG Lu*.The Dynamic Pruning Collaborative Sparse Classification and Its Application[J].Journal of Jiangxi Normal University:Natural Science Edition,2020,(05):472-477.[doi:10.16357/j.cnki.issn1000-5862.2020.05.04]
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一种动态剪枝的协同稀疏表示方法研究及应用()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年05期
页码:
472-477
栏目:
信息科学与技术
出版日期:
2020-10-20

文章信息/Info

Title:
The Dynamic Pruning Collaborative Sparse Classification and Its Application
文章编号:
1000-5862(2020)05-0472-06
作者:
周俊星梁 路*
广东工业大学计算机学院,广东 广州 510006
Author(s):
ZHOU JunxingLIANG Lu*
College of Computer,Guangdong University of Technology,Guangzhou 510006,China
关键词:
模式识别 生物特征识别 稀疏表示 协同表示
Keywords:
pattern recognition biometrics sparse representation collaborative representation
分类号:
TP 391
DOI:
10.16357/j.cnki.issn1000-5862.2020.05.04
文献标志码:
A
摘要:
该文提出一种动态剪枝的协同稀疏表示方法,通过建立2种不同的训练样本筛选策略,再融合2种策略的优点及结合TPTSR框架进行图像识别,以求获得更好的分类效果.在带噪声的人脸数据集上进行对比实验,结果表明:该方法可以在人脸受到遮挡和光照变化的影响下达到更高的识别率,并具有较强的鲁棒性.
Abstract:
The dynamic pruning collaborative sparse classification(DPCSC)method that is two flexible strategies of selecting suitable and competitive training,samples for sparse representation are built separately has been proposed,and also can be combined with TPTSR framework in image recognition for achieving better performance.Extensive experiments conducted on publicly available face datasets with noise clearly show that the proposed DPCSC performs excellent accuracy under the influences of occlusion and illumination variations on the face image,and robustness for face images with illumination and occlusion.

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备注/Memo

备注/Memo:
收稿日期:2020-03-29
基金项目:国家自然科学基金(61402118,61673123)和广东省重点领域研发计划(2020B010166006)资助项目.
通信作者:梁 路(1980-),女,江西南昌人,副教授,主要从事人工智能、协同计算、数据挖掘方面的研究.E-mail:
更新日期/Last Update: 2020-10-20