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[1] Wu Dan, Wu Jiasong, , et al. Kernel principal component analysis networkfor image classification [J]. Journal of Southeast University (English Edition), 2015, 31 (4): 469-473. [doi:10.3969/j.issn.1003-7985.2015.04.007]
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Kernel principal component analysis networkfor image classification()
面向图像分类的核主成分分析网络
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
31
Issue:
2015 4
Page:
469-473
Research Field:
Computer Science and Engineering
Publishing date:
2015-12-30

Info

Title:
Kernel principal component analysis networkfor image classification
面向图像分类的核主成分分析网络
Author(s):
Wu Dan1;  4;  Wu Jiasong1;  2;  3;  4;  Zeng Rui1;  4;  Jiang Longyu1;  4Lotfi Senhadji2;  3;  4;  Shu Huazhong1;  4
1Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing 210096, China
2Institut National de la Santé et de la Recherche Médicale U 1099, Rennes 35000, France
3Laboratoire Traitement du Signal et de l’Image, Université de Rennes 1, Rennes 35000, France
4Centre de Recherche en Information Biomédicale Sino-Français, Nanjing 210096, China
吴丹1;  4;  伍家松1;  2;  3;  4;  曾瑞1;  4;  姜龙玉1;  4;  Lotfi Senhadji2;  3;  4;  舒华忠1;  4
1东南大学计算机网络和信息集成教育部重点实验室, 南京 210096; 2Institut National de la Santé et de la Recherche Médicale U 1099, Rennes 35000, France; 3Laboratoire Traitement du Signal et de l’Image, Université de Rennes 1, Rennes 35000, France; 4中法生物医学信息研究中心, 南京 210096
Keywords:
deep learning;  kernel principal component analysis net(KPCANet);  principal component analysis net(PCANet);  face recognition;  object recognition;  handwritten digit recognition
深度学习;  核主成分分析网络;  主成分分析网络;  人脸识别;  物体识别;  手写数字识别
PACS:
TP391
DOI:
10.3969/j.issn.1003-7985.2015.04.007
Abstract:
In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network(KPCANet)is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results show that the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network(PCANet)generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
为了能够用线性分类器对非线性特征进行分类, 同时提高图像的分类正确率, 提出了一种核主成分分析网络(KPCANet).首先通过核主成分分析算法将数据映射到高维空间中, 使得数据线性可分, 然后建立一个2层的KPCANet, 提取出图像的主特征, 最后将图像的主特征输入线性分类器中进行分类. 实验结果表明, KPCANet对于人脸识别、物体识别以及手写数字识别效果良好, 其分类效果优于现存的主成分分析网络(PCANet).同时, KPCANet的成分提取效果不受光照条件变化的影响, 且对于遮挡以及微小的形变提取效果稳定.

References:

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Memo

Memo:
Biographies: Wu Dan(1990—), female, graduate; Shu Huazhong(corresponding author), male, doctor, professor, shu.list@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61201344, 61271312, 61401085, 11301074), the Research Fund for the Doctoral Program of Higher Education(No.20120092120036), the Program for Special Talents in Six Fields of Jiangsu Province(No.DZXX-031), Industry-University-Research Cooperation Project of Jiangsu Province(No.BY2014127-11), “333” Project(No.BRA2015288), High-End Foreign Experts Recruitment Program(No.GDT20153200043), Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404).
Citation: Wu Dan, Wu Jiasong, Zeng Rui, et al. Kernel principal component analysis network for image classification[J].Journal of Southeast University(English Edition), 2015, 31(4):469-473.[doi:10.3969/j.issn.1003-7985.2015.04.007]
Last Update: 2015-12-20