Journal
PATTERN RECOGNITION
Volume 44, Issue 5, Pages 1031-1040Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.11.004
Keywords
Pattern recognition; Canonical correlation analysis; Feature extraction; Multiset canonical correlation analysis; Feature fusion
Funding
- National Science Foundation of China
- National Science Foundation of Jiangsu Province
- Ministry of Education of China(RFDP) [60773172, BK2008411, 200802880017]
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Multiset canonical correlation analysis (MCCA) is difficult to effectively express the integrated correlation among multiple feature vectors in feature fusion. Thus, this paper firstly presents a novel multiset integrated canonical correlation analysis (MICCA) framework. The MICCA establishes a discriminant correlation criterion function of multi-group variables based on generalized correlation coefficient. The criterion function can clearly depict the integrated correlation among multiple feature vectors. Then the paper presents a multiple feature fusion theory and algorithm using the MICCA method. The detailed process of the algorithm is as follows: firstly, extract multiple feature vectors from the same patterns by using different feature extraction methods; then extract multiset integrated canonical correlation features using MICCA; finally form effective discriminant feature vectors through two given feature fusion strategies for pattern classification. The multi-group feature fusion method based on MICCA not only achieves the aim of feature fusion, but also removes the redundancy between features. The experiment results on CENPARMI handwritten Arabic numerals and UCI multiple features database show that the MICCA method has better recognition rates and robustness than the fusion methods based on canonical correlation analysis (CCA) and MCCA. (C) 2010 Elsevier Ltd. All rights reserved.
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