Manifold Discriminant Analysis

• Published in: 2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 429 - 436
• Main Authors: Ruiping Wang, Xilin Chen
• Format: Conference Proceeding
• Language: English
• 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
• DOI: 10.1109/CVPR.2009.5206850
• ISBN: 1424439922; 9781424439928
• ISSN: 1063-6919

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.