Feature extraction based on fuzzy 2DLDA

Authors:
Wankou Yang;Xiaoyong Yan;Lei Zhang;Changyin Sun
Affiliations:
School of Automation, Southeast University, Nanjing 210096, People's Republic of China;School of Information Technology, Jinling Institute of Technology, Nanjing 210001, People's Republic of China;Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong;School of Automation, Southeast University, Nanjing 210096, People's Republic of China
Venue:
Neurocomputing
Year:
2010

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Abstract

In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. In our definition of the between-class scatter matrix and within-class matrix, the fuzzy information is better used than fuzzy fisherface. Experiments on the Yale, ORL and FERET face databases show that the new method works well.