Two dimension double PCA for extracting features and application based on between-class scatter matrix

Ruiping Zhang; Dongsheng Li

doi:10.1117/12.906299

28 October 2011 Two dimension double PCA for extracting features and application based on between-class scatter matrix

Ruiping Zhang, Dongsheng Li

Proceedings Volume 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization; 82050Y (2011) https://doi.org/10.1117/12.906299
Event: 2011 International Conference on Photonics, 3D-imaging, and Visualization, 2011, Guangzhou, China

Abstract

Conventional PCA usually uses total scatter matrix as a generation matrix, and two dimension image matrices must be transformed into vectors. In this paper, the between-class matrix generated by original image and its eigenvectors were used to feature extracting. First we compressed the image in horizon direction using 2DPCA, then we compressed the feature matrix in vertical direction. Thus, the dimension of features is lesser and the speed of classification is faster. At the same time the category information is fully used and the recognition rate are improved.

Citation Download Citation

Ruiping Zhang and Dongsheng Li "Two dimension double PCA for extracting features and application based on between-class scatter matrix", Proc. SPIE 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization, 82050Y (28 October 2011); https://doi.org/10.1117/12.906299

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KEYWORDS

Feature extraction

Principal component analysis

Image compression

Databases

Bismuth

Digital imaging

Facial recognition systems

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