A multi-manifold discriminant analysis method for image feature extraction

In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively,...

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Bibliographic Details
Published in:Pattern recognition 2011-08, Vol.44 (8), p.1649-1657
Main Authors: Yang, Wankou, Sun, Changyin, Zhang, Lei
Format: Article
Language:English
Subjects:
Applied sciences
Discriminant analysis
Exact sciences and technology
Feature extraction
Graphs
Image recognition
Information theory
Information, signal and communications theory
LDA
Learning
Miscellaneous
Multi-manifold learning
Pattern recognition
Recognition
Scatter
Signal processing
Telecommunications and information theory
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Summary:In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively, designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in an MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR and ORL face databases, and the PolyU finger-knuckle-print databases. The experimental results demonstrate that an MMDA is effective in feature extraction, leading to promising image recognition performance. ► We proposed a multi-manifold discriminant analysis (MMDA) method for the feature extraction. ► In an MMDA, two graphs are constructed to model multi-manifolds for classification. ► An MMDA is based on graph embedded learning and is under the Fisher discriminant analysis framework. ► An MMDA is evaluated on three benchmark face databases and the PolyU FKP database.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2011.01.019