TY - GEN
T1 - Two-dimensional technique for image presentation and its application
AU - Xu, Yong
AU - Yang, Jing Yu
AU - Tang, Zhen Min
AU - Zhao, Chun Xia
PY - 2006
Y1 - 2006
N2 - In contrast with PCA, the two-dimension presentation technique (TDP) developed recently is very efficient. With TDP, we can easily extract feature vectors of an image matrix by projecting the image matrix rather than the corresponding vector onto projecting axes. In this paper, we present complete properties of TDP in detail and the property of decorrelation associated with TDP is originally revealed. The differences and similarities between TDP and PCA are also analyzed and presented. Furthermore, Local-TDP approach is proposed to perform face recognition. Local-TDP aims to draw local characteristic of face images. Especially, Local-TDP appears to be beneficial to weaken the side effect on face recognition of varying imaging conditions. The possible reason is that the varying imaging conditions mainly bring strong difference for parts of the image, while the influence on other parts is little. As a result, the similarity between the extracted local features of two face images of one individual may become larger in comparison with holistic features of face images. The conducted experiment also indicates that Local-TDP is competent for extracting invariant features of face images with varying illumination.
AB - In contrast with PCA, the two-dimension presentation technique (TDP) developed recently is very efficient. With TDP, we can easily extract feature vectors of an image matrix by projecting the image matrix rather than the corresponding vector onto projecting axes. In this paper, we present complete properties of TDP in detail and the property of decorrelation associated with TDP is originally revealed. The differences and similarities between TDP and PCA are also analyzed and presented. Furthermore, Local-TDP approach is proposed to perform face recognition. Local-TDP aims to draw local characteristic of face images. Especially, Local-TDP appears to be beneficial to weaken the side effect on face recognition of varying imaging conditions. The possible reason is that the varying imaging conditions mainly bring strong difference for parts of the image, while the influence on other parts is little. As a result, the similarity between the extracted local features of two face images of one individual may become larger in comparison with holistic features of face images. The conducted experiment also indicates that Local-TDP is competent for extracting invariant features of face images with varying illumination.
KW - Correlation coefficient
KW - PCA
KW - Reconstruction error
KW - Uncorrelated features
UR - https://www.scopus.com/pages/publications/33947202222
U2 - 10.1109/ICMLC.2006.259088
DO - 10.1109/ICMLC.2006.259088
M3 - 会议稿件
AN - SCOPUS:33947202222
SN - 1424400619
SN - 9781424400614
T3 - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
SP - 4376
EP - 4382
BT - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
PB - IEEE Computer Society
T2 - 5th International Conference on Machine Learning and Cybernetics, ICMLC 2006
Y2 - 13 August 2006 through 16 August 2006
ER -