Unified Discriminative Subspace Learning for Multimodality Image Analysis

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Fu, Yun

Contributors dc:contributor

• Huang, Thomas S.

• Artificial Intelligence

Language dc:language

eng

Identifier

(MiAaPQ)AAI3337764

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81093