Manifold Discriminant Analysis

This paper presents a novel discriminative learning method, called manifold discriminant analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we formulate the problem as classification-oriented multi-manifolds learning. Aiming at maximizing &quo...

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Bibliographic Details
Published in2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 429 - 436
Main Authors Ruiping Wang, Xilin Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Computers
Content addressable storage
Image analysis
Image recognition
Information analysis
Information processing
Laplace equations
Linear discriminant analysis
Object recognition
Testing
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Summary:This paper presents a novel discriminative learning method, called manifold discriminant analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we formulate the problem as classification-oriented multi-manifolds learning. Aiming at maximizing "manifold margin", MDA seeks to learn an embedding space, where manifolds with different class labels are better separated, and local data compactness within each manifold is enhanced. As a result, new testing manifold can be more reliably classified in the learned embedding space. The proposed method is evaluated on the tasks of object recognition with image sets, including face recognition and object categorization. Comprehensive comparisons and extensive experiments demonstrate the effectiveness of our method.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206850