Image Recognition with Two-Dimensional Neighbourhood Preserving Embedding
ZHANG Da-Ming 1,2, FU Mao-Sheng1, LUO Bin1
1.School of Computer Science and Technology, Anhui University, Hefei 230039 2.Department of Mathematics and Physics, Anhui Institute of Architecture and Industry, Hefei 230022
Abstract Neighbourhood preserving embedding (NPE) is a subspace learning algorithm, which aims at preserving the local neighbourhood structure on the data manifold, and it is a linear approximation to Locally Linear Embedding (LLE). When image data are concerned, the dimensionality of vectorized image data is usually high. NPE can not be implemented due to singularity of matrix. NPE is extended to 2 dimensional senses, 2DNPE, which directly extracts image feature from 2D image matrices rather than from 1D vectors as NPE does. The proposed algorithm is evaluated on Yale face database and Binary Alpha digits database.
[1] Duda R O, Hart P E, Stork D G. Pattern Classification. 2nd Edition. New York, USA: John Wiley Sons, 2000 [2] Tenenbaum J, de Silva V, Langford J. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323 [3] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326 [4] Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 2003, 15(6): 1373-1396 [5] Zhang Zhenyue, Zha Hongyun. Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment. SIAM Journal on Scientific Computing, 2004, 26(1): 313-338 [6] He Xiaofei, Niyogi P. Locality Preserving Projections[EB/OL]. [2003-12-10]. http://Books.nips.cc/papers/files/nips16/NIPS2003-AA20.pdf [7] He Xiaofei, Yan Shuicheng, Hu Yuxiao, et al. Face Recognition Using Laplacian Faces. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340 [8] He Xiaofei, Cai Deng, Yan Shuicheng, et al. Neighborhood Preserving Embedding // Proc of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005, II: 1208-1213 [9] Yang Jian, Zhang D, Frangi A F, et al. Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(1): 1-7 [10] Li Ming, Yuan Baozong. 2D-LDA: A Statistical Linear Discriminant Analysis for Image Matrix. Pattern Recognition Letters, 2005, 26(5): 527-532. [11] Chen Sibao, Zhao Haifeng, Kong Min, et al. 2D-LPP: Two-Dimensional Extension of Locality Preserving Projections. Neurocomputing, 2007, 70(4/5/6): 912-921 [12] Liu Xiaoming, Yin Jianwei, Feng Zhilin, et al. Orthogonal Neighborhood Preserving Embedding for Face Recognition // Proc of the IEEE International Conference on Image Processing. San Antonio, USA, 2007, I: 133-136 [13] Wang Ziqiang, Sun Xia. Face Recognition Using Kernel-Based NPE // Proc of the International Conference on Computer Science and Software Engineering. Wuhan, China, 2008, I: 802-805 [14] Tao Xiaoyan, Ji Hongbing, Men Jian. Kernel Neighborhood Preserving Embedding for Classification. Journal of Electronics (China), 2009, 26(3): 374-379 [15] Chen Sibao, Zhao Haifeng, Luo Bin. LPP/QR for Under-Sampled Image Recognition // Proc of the International Conference on Machine Learning and Cybernetics. Guangzhou, China, 2005, VII: 4558-4563
2011, Vol. 24 
