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Proceedings Paper

Two-dimensional reduction PCA: a novel approach for feature extraction, representation, and recognition
Author(s): R. M. Mutelo; L. C. Khor; W. L. Woo; S. S. Dlay
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Paper Abstract

We develop a novel image feature extraction and recognition method two-dimensional reduction principal component analysis (2D-RPCA)). A two dimension image matrix contains redundancy information between columns and between rows. Conventional PCA removes redundancy by transforming the 2D image matrices into a vector where dimension reduction is done in one direction (column wise). Unlike 2DPCA, 2D-RPCA eliminates redundancies between image rows and compresses the data in rows, and finally eliminates redundancies between image columns and compress the data in columns. Therefore, 2D-RPCA has two image compression stages: firstly, it eliminates the redundancies between image rows and compresses the information optimally within a few rows. Finally, it eliminates the redundancies between image columns and compresses the information within a few columns. This sequence is selected in such a way that the recognition accuracy is optimized. As a result it has a better representation as the information is more compact in a smaller area. The classification time is reduced significantly (smaller feature matrix). Furthermore, the computational complexity of the proposed algorithm is reduced. The result is that 2D-RPCA classifies image faster, less memory storage and yields higher recognition accuracy. The ORL database is used as a benchmark. The new algorithm achieves a recognition rate of 95.0% using 9×5 feature matrix compared to the recognition rate of 93.0% with a 112×7 feature matrix for the 2DPCA method and 90.5% for PCA (Eigenfaces) using 175 principal components.

Paper Details

Date Published: 16 January 2006
PDF: 10 pages
Proc. SPIE 6060, Visualization and Data Analysis 2006, 60600E (16 January 2006); doi: 10.1117/12.650555
Show Author Affiliations
R. M. Mutelo, Univ. of Newcastle (United Kingdom)
L. C. Khor, Univ. of Newcastle (United Kingdom)
W. L. Woo, Univ. of Newcastle (United Kingdom)
S. S. Dlay, Univ. of Newcastle (United Kingdom)


Published in SPIE Proceedings Vol. 6060:
Visualization and Data Analysis 2006
Robert F. Erbacher; Jonathan C. Roberts; Matti T. Gröhn; Katy Börner, Editor(s)

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