Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-based Object Recognition Tasks

Recently, a number of empirical studies have compared the performance of PCA and ICA as feature extraction methods in appearance-based object recognition systems, with mixed and seemingly contradictory results. In this paper, we briefly describe the connection between the two methods and argue that whitened PCA may yield identical results to ICA in some cases. Furthermore, we describe the specific situations in which ICA might significantly improve on PCA

[1]  Hiroshi Murase,et al.  Subspace methods for robot vision , 1996, IEEE Trans. Robotics Autom..

[2]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources , 1999, Neural Comput..

[3]  H. Wechsler,et al.  Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition , 1999 .

[4]  Patrick J. Flynn,et al.  A Survey Of Free-Form Object Representation and Recognition Techniques , 2001, Comput. Vis. Image Underst..

[5]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[6]  Jeff Fortuna,et al.  A comparison of PCA and ICA for object recognition under varying illumination , 2002, Object recognition supported by user interaction for service robots.

[7]  Jian-Huang Lai,et al.  Independent Component Analysis of Face Images , 2000, Biologically Motivated Computer Vision.

[8]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[9]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[10]  P. Comon.  Independent Component Analysis , 1992 .

[11]  Tiziana D'Orazio,et al.  Face Recognition by Kernel Independent Component Analysis , 2005, IEA/AIE.

[12]  Jordi Vitrià,et al.  On the Selection and Classification of Independent Features , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  D. Capson,et al.  A comparison of subspace methods for accurate position measurement , 2004, 6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004..

[14]  S. Edelman,et al.  Computational Theories of Object Recognition Edelman -computation and Object Recognition Ii Box 1. Structural Descriptions ~ 7~ Recognition by Components Varieties of Alignment Multidimensional Histograms Approximation in Feature Space , 2022 .

[15]  Khashayar Khorasani,et al.  A neural-network appearance-based 3-D object recognition using independent component analysis , 2003, IEEE Trans. Neural Networks.

[16]  Chengjun Liu,et al.  Enhanced independent component analysis and its application to content based face image retrieval , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[18]  Bülent Sankur,et al.  ARTICLE IN PRESS Image and Vision Computing xx (2005) 1–9 www.elsevier.com/locate/imavis , 2004 .

[19]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[20]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Andrea Salgian,et al.  A comparative analysis of face recognition performance with visible and thermal infrared imagery , 2002, Object recognition supported by user interaction for service robots.

[22]  Chengjun Liu,et al.  Independent component analysis of Gabor features for face recognition , 2003, IEEE Trans. Neural Networks.

[23]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[24]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[25]  Bruce A. Draper,et al.  Recognizing faces with PCA and ICA , 2003, Comput. Vis. Image Underst..

[26]  Katsushi Ikeuchi,et al.  Detectability, Uniqueness, and Reliability of Eigen Windows for Stable Verification of Partially Occluded Objects , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[28]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[30]  Baback Moghaddam,et al.  Principal Manifolds and Probabilistic Subspaces for Visual Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Bruce A. Draper,et al.  PCA vs. ICA: A Comparison on the FERET Data Set , 2002, JCIS.

[32]  Luis Payá,et al.  3D Object Recognition from Appearance: PCA Versus ICA Approaches , 2004, ICIAR.

[33]  Jian Yang,et al.  Is ICA significantly better than PCA for face recognition? , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.