期刊
PATTERN RECOGNITION
卷 36, 期 6, 页码 1369-1381出版社
ELSEVIER SCI LTD
DOI: 10.1016/S0031-3203(02)00262-5
关键词
feature fusion; feature extraction; complex feature space; principal component analysis (PCA); K-L expansion; linear discriminant analysis (LDA); character recognition; face recognition
A new strategy of parallel feature fusion is introduced in this paper. A complex vector is first used to represent the parallel combined features. Then, the traditional linear projection analysis methods, including principal component analysis, K-L expansion and linear discriminant analysis, are generalized for feature extraction in the complex feature space. Finally, the developed parallel feature fusion methods are tested on CENPARMI handwritten numeral database, NUST603 handwritten Chinese character database and ORL face image database. The experimental results indicate that the classification accuracy is increased significantly under parallel feature fusion and also demonstrate that the developed parallel fusion is more effective than the classical serial feature fusion. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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