基于圖像矩陣的廣義主分量分析
A Generalized Principal Component Analysis Based on Image Matrix
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摘要: 傳統(tǒng)的主分量分析在處理圖像識別問題時是基于向量的，且沒有充分利用訓(xùn)練樣本的類別信息。該文提出了一種直接基于圖像矩陣的廣義主分量分析方法，該方法能夠提取包含在類平均圖像中的鑒別信息，與傳統(tǒng)的主分量分析相比，具有更強的鑒別力。在ORL標(biāo)準(zhǔn)人臉庫上的試驗結(jié)果表明，所提出的方法不僅識別性能優(yōu)于傳統(tǒng)的主分量分析和Fisher線性鑒別分析，而且極大地提高了特征抽取的速度。
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關(guān)鍵詞:
- 圖像識別; 主分量分析; 圖像矩陣; 特征抽取
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. -
Pentland A. Looking at people: sensing for ubiquitous and wearable computing[J].IEEE Trans.Pattern Anal. Machine Intell.2000, 22(1):107-119[2]Grudin M A. On internal representations in face recognition systems[J].Pattern Recognition.2000, 33(7):1161-1177[3]Turk M, Pentland A. Face recognition using eigenfaces[C]. Proc. IEEE Conf. On Computer Vision and Pattern Recognition, Hawaii, 1991: 586-591.[4]Liu K, Cheng Y.-Q., Yang J.-Y., et al.. Algebraic feature extraction for image recognition based on an optimal discriminant criterion. Pattern Recognition, 1993, 26(6): 903-911.[5]Belhumeur P N, Hespanha J P, Kriengman D J. Eigenfaces vsFisherfaces: Recognition using class specific linear projection[J].. IEEE Trans. on Pattern Anal. Machine Intell.1997, 19(7):711-720[6]Pentland A, Moghaddam B, Starner T. View-based and mododular eigenspaces for face recognition. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1994: 84-91.[7]程云鵬.矩陣論.西安:西北工業(yè)大學(xué)出版社,1999:294-302. -
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