A Generalized Principal Component Analysis Based on Image Matrix

Chen Cai-kou; Yang Jian; Yang Jing-yu; Gao Xiu-mei

Volume 26 Issue 12

Dec. 2004

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Article Navigation > Journal of Electronics & Information Technology > 2004 > 26(12): 1871-1874

Chen Cai-kou, Yang Jian, Yang Jing-yu, Gao Xiu-mei. A Generalized Principal Component Analysis Based on Image Matrix[J]. Journal of Electronics & Information Technology, 2004, 26(12): 1871-1874.

Citation:

Chen Cai-kou, Yang Jian, Yang Jing-yu, Gao Xiu-mei. A Generalized Principal Component Analysis Based on Image Matrix[J]. Journal of Electronics & Information Technology, 2004, 26(12): 1871-1874.

Chen Cai-kou, Yang Jian, Yang Jing-yu, Gao Xiu-mei. A Generalized Principal Component Analysis Based on Image Matrix[J]. Journal of Electronics & Information Technology, 2004, 26(12): 1871-1874.

Citation:

Chen Cai-kou, Yang Jian, Yang Jing-yu, Gao Xiu-mei. A Generalized Principal Component Analysis Based on Image Matrix[J]. Journal of Electronics & Information Technology, 2004, 26(12): 1871-1874.

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A Generalized Principal Component Analysis Based on Image Matrix

Received Date: 2003-07-21
Rev Recd Date: 2003-10-08
Publish Date: 2004-12-19

Abstract

Abstract

The classical Principal Component Analysis (PCA) for image feature extraction is usually based on vectors, which makes it very time-consuming, and the class information in the training sample has not been utilized fully also. To overcome these two drawbacks of PCA, this paper proposes a novel and efficient PCA method based on original image matri-ces directly. It can extract the discriminant information included in the class mean images. Hence, the proposed method has better discriminant performance than classical PCA. Ex-perimental results on ORL face database show the proposed method is more powerful and efficient than the classical PCA and Fisher linear discriminant analysis.

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References(1)

References

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