Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12723
Title: A novel supervised dimensionality reduction algorithm for online image recognition
Authors: Song, F
Zhang, D 
Chen, Q
Yang, J
Keywords: Dimensionality reduction
Image recognition
Incremental algorithm
Streaming data
Supervised learning
Issue Date: 2006
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 4319 LNCS, p. 198-207 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Image recognition on streaming data is one of the most challenging topics in Image and Video Technology and incremental dimensionality reduction algorithms play a key role in online image recognition. In this paper, we present a novel supervised dimensionality reduction algorithm-Incremental Weighted Karhunen-Loève expansion based on the Between-class scatter matrix (IWKLB) for image recognition on streaming data. In comparison with Incremental PCA, IWKLB is more effective in terms of recognition rate. In comparison with Incremental LDA, it is free of small sample size problems and can directly be applied to high-dimensional image spaces with high efficiency. Experimental results conducted on AR, one benchmark face image database, demonstrate that IWKLB is more effective than IPCA and ILDA.
Description: 1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006, Hsinchu, 10-13 December 2006
URI: http://hdl.handle.net/10397/12723
ISBN: 354068297X
9783540682974
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11949534-20
Appears in Collections:Conference Paper

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