Two-dimensional discriminant locality preserving projections (2DDLPP) and its application to feature extraction via fuzzy set

被引：0
作者：
Minghua Wan
Guowei Yang
Shan Gai
Zhangjing Yang
机构：
[1] Nanjing Audit University,School of Technology
[2] Nanjing University of Science and Technology,Key Laboratory of Intelligent Perception and Systems for High
[3] Nanjing Xiaozhuang University,Dimensional Information of Ministry of Education
来源：
Multimedia Tools and Applications | 2017年 / 76卷
关键词：
2DDLPP; Fuzzy set theory; Feature extraction; FKNN; Membership degree;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a new method for image feature extraction, namely, the fuzzy 2D discriminant locality preserving projections (F2DDLPP) based on the 2D discriminant locality preserving projections (2DDLPP) and fuzzy set theory. Firstly, we calculate the membership degree matrix by fuzzy k-nearest neighbor (FKNN), then we incorporate the membership degree matrix into the definition of the intra-class scatter matrix and inter-class scatter matrix, respectively. Secondly, we can get the fuzzy intra-class scatter matrix and fuzzy inter-class scatter matrix, respectively. The FKNN is implemented to achieve the distribution information of original samples, and this information is utilized to redefine corresponding scatter matrices. So, F2DDLPP can extract discriminative features from overlapping (outlier) samples which is different to the conventional 2DDLPP. Finally, Experiments on the Yale, ORL face databases, USPS database and PolyU palmprint database are demonstrated to verify the effectiveness of the proposed algorithm.
引用
收藏
页码：355 / 371
页数：16
相关论文
共 92 条
  • [11] Ko MT(1985)A fuzzy k-nearest neighbour algorithm IEEE Trans Syst Man Cybern 15 580-585
  • [12] Yu GJ(2005)Face recognition using a fuzzy fisher classifier Pattern Recogn 38 1717-1732
  • [13] Chen S(2005)2D-LDA: a statistical linear discriminant analysis for image matrix Pattern Recogn Lett 26 527-532
  • [14] Zhao H(2008)Two-dimensional Laplacianfaces method for face recognition Pattern Recogn 41 3237-3243
  • [15] Kong M(1991)Small sample size effects in statistical pattern recognition: recommendations for practitioners IEEE Trans Pattern Anal Mach Intell 13 252-264
  • [16] Luo B(2000)Nonlinear dimension reduction by locally linear embedding Science 290 2323-2326
  • [17] Chowdhury S(2014)Learning a two-dimensional fuzzy discriminant locality preserving subspace for visual recognition [J] IEEE Trans Inf Syst 97 2434-2442
  • [18] Sing JK(2000)A global geometric framework for nonlinear dimensionality reduction Science 290 2319-2323
  • [19] Basu DK(2009)Two-dimensional local graph embedding discriminant analysis (2DLGEDA) with its application to face and palm biometrics Neurocomputing 73 193-203
  • [20] He X(2012)Feature extraction based on fuzzy class mean embedding (FCME) with its application to face and palm biometrics Mach Vis Appl 23 985-997
12345678910