Structural two-dimensional principal component analysis for image recognition |
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Authors: | Haixian Wang |
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Affiliation: | (1) Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing, Jiangsu, 210096, China;(2) Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, 230027, China;(3) School of Mathematics and Physics, Anhui University of Technology, Maanshan, Anhui, 243002, China;(4) Key Lab of ICSP of Ministry of Education, Anhui University, Hefei, Anhui, 230039, China |
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Abstract: | In this paper, a new technique called structural two-dimensional principal component analysis (S2DPCA) is proposed for image
recognition. S2DPCA is a subspace learning method that identifies the structural information for discrimination. Different
from conventional two-dimensional principal component analysis (2DPCA) that only reflects within-row information of images,
the goal of S2DPCA is to discover structural discriminative information contained in both within-row and between-row of the
images. By contrast with 2DPCA, S2DPCA is directly based on the augmented images encoding corresponding row membership, and
the projection directions of S2DPCA are obtained by solving an eigenvalue problem of the augmented image covariance matrix.
Computationally, S2DPCA is straightforward and comparative with 2DPCA. Like 2DPCA, the singularity problem is completely avoided
in S2DPCA. Experiments on face recognition and handwritten digit recognition are presented to show the effectiveness of the
proposed approach. |
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Keywords: | |
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