Unified Discriminative Subspace Learning for Multimodality Image Analysis
Fu, Yun
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Permalink
https://hdl.handle.net/2142/81093
Description
Title
Unified Discriminative Subspace Learning for Multimodality Image Analysis
Author(s)
Fu, Yun
Issue Date
2008
Doctoral Committee Chair(s)
Huang, Thomas S.
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Language
eng
Abstract
To demonstrate the effectiveness of the framework, an expert model of the query-driven locally adaptive (QDLA) method and four new subspace learning algorithms corresponding to different learning-locality criteria are presented. These four algorithms are locally embedded analysis (LEA), discriminant simplex analysis (DSA), correlation embedding analysis (CEA), and correlation tensor analysis (CTA). Extensive experiments demonstrate that applying the local manner in the sample space, feature space, and learning space can sufficiently boost the discriminating power for feature extraction by the subspace learning. As an advanced extension, a learning-locality based subspace learning algorithm for multiple/multimodality feature fusion is also developed in both unsupervised and supervised learning cases. Those methods are successfully applied to several real-world applications of facial image computing, such as face recognition, head pose estimation, realistic expression/emotion analysis, human age estimation, and lipreading.
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