Two-dimensional reduction PCA: a novel approach for feature extraction, representation, and recognition

R. M. Mutelo; L. C. Khor; W. L. Woo; S. S. Dlay

doi:10.1117/12.650555

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16 January 2006 Two-dimensional reduction PCA: a novel approach for feature extraction, representation, and recognition

R. M. Mutelo, L. C. Khor, W. L. Woo, S. S. Dlay

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Proceedings Volume 6060, Visualization and Data Analysis 2006; 60600E (2006) https://doi.org/10.1117/12.650555
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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.

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R. M. Mutelo, L. C. Khor, W. L. Woo, and S. S. Dlay "Two-dimensional reduction PCA: a novel approach for feature extraction, representation, and recognition", Proc. SPIE 6060, Visualization and Data Analysis 2006, 60600E (16 January 2006); https://doi.org/10.1117/12.650555

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