13.4.1.4 Invariants -- Principal Component Analysis

Chapter Contents (Back)
PCA. Object Recognition. Principal Components. PCA is optimal for pattern representation, not classification. Computation: See also Computation and Analysis of Principal Components, Eigen Values, SVD.

Jolliffe, I.T.,
Principal Component Analysis,
SpringerNew-York, 2002. ISBN: 978-0-387-95442-4.
WWW Link. Buy this book: Principal Component Analysis BibRef 0200
Earlier: First edition: Springer-VerlagNew-York, 1986. Survey, PCA. The Book, overview. BibRef

Shimura, M.[Masamichi], Imai, T.[Toshio],
Nonsupervised classification using the principal component,
PR(5), No. 4, December 1973, pp. 353-363.
Elsevier DOI 0309
BibRef

Wold, S.[Svante],
Pattern recognition by means of disjoint principal components models,
PR(8), No. 3, July 1976, pp. 127-139.
Elsevier DOI 0309
BibRef

Chang, C.C., Chen, Y.W., Buehrer, D.J.,
A Two-Dimensional Shape Recognition Scheme Based on Principal Component Analysis,
PRAI(8), 1994, pp. 859-875. BibRef 9400

Chen, C.Y., Chang, C.C., and Lee, R.C.T.,
A Near Pattern-Matching Scheme Based upon Principal Component Analysis,
PRL(16), 1995, pp. 339-345. See also Exact Match Retrieval Scheme Based Upon Principal Component Analysis, An. BibRef 9500

Chang, C.I.[C. I], Du, Q.,
Interference and Noise-Adjusted Principal Components Analysis,
GeoRS(37), No. 5, September 1999, pp. 2387.
IEEE Top Reference. BibRef 9909

Weingessel, A.[Andreas], Bischof, H.[Horst], Hornik, K.[Kurt], Leisch, F.[Friedrich],
Adaptive Combination of PCA and VQ Networks,
TNN(8), No. 5, 1997, pp. 1208-1211. BibRef 9700
Earlier:
Hierarchies of Autoassociators,
ICPR96(IV: 200-204).
IEEE DOI 9608
(Technical Univ. of Vienna, A) BibRef

Crowley, J.L., Pourraz, F.,
Continuity properties of the appearance manifold for mobile robot position estimation,
IVC(19), No. 11, September 2001, pp. 741-752.
Elsevier DOI PCA. Orthogonal basis to represent appearance from different positions. 0108
BibRef

Twining, C.J., Taylor, C.J.,
The use of kernel principal component analysis to model data distributions,
PR(36), No. 1, January 2003, pp. 217-227.
WWW Link. 0210
BibRef

Glendinning, R.H., Herbert, R.A.,
Shape classification using smooth principal components,
PRL(24), No. 12, August 2003, pp. 2021-2030.
Elsevier DOI 0304
BibRef

de la Torre, F.[Fernando], Black, M.J.[Michael J.],
Robust Parameterized Component Analysis: Theory and Applications to 2D Facial Appearance Models,
CVIU(91), No. 1-2, July-August 2003, pp. 53-71.
Elsevier DOI
PDF File. 0309
BibRef
Earlier:
Robust Parameterized Component Analysis,
ECCV02(IV: 653 ff.).
Springer DOI 0205
BibRef
Earlier:
Robust Principal Component Analysis for Computer vision,
ICCV01(I: 362-369).
IEEE DOI 0106
BibRef
And:
Dynamic Coupled Component Analysis,
CVPR01(II:643-650).
IEEE DOI 0110
BibRef

de la Torre, F., Kanade, T.,
Oriented Discriminant Analysis,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Chen, Y.[Ying], de la Torre, F.[Fernando],
Active conditional models,
FG11(137-142).
IEEE DOI 1103
Faces with different pose and expression. ACM: combine features and ASM BibRef

Sengel, M., Bischof, H.,
Efficient representation of in-plane rotation within a PCA framework,
IVC(23), No. 12, 1 November 2005, pp. 1051-1059.
Elsevier DOI 0510
BibRef

Vidal, R., Ma, Y.[Yi], Sastry, S.,
Generalized Principal Component Analysis (GPCA),
PAMI(27), No. 12, December 2005, pp. 1945-1959.
IEEE DOI 0512
BibRef
Earlier: CVPR03(I: 621-628).
IEEE DOI 0307
BibRef

Vidal, R., Ma, Y.[Yi], Piazzi, J.,
A new GPCA algorithm for clustering subspaces by fitting, differentiating and dividing polynomials,
CVPR04(I: 510-517).
IEEE DOI 0408
BibRef

Nagabhushan, P., Guru, D.S., Shekar, B.H.,
Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach,
PR(39), No. 4, April 2006, pp. 721-725.
Elsevier DOI Principal component analysis; Appearance based model; Object recognition 0604
BibRef
Earlier: A3, A2, A1:
Object Recognition Through the Principal Component Analysis of Spatial Relationship Amongst Lines,
ACCV06(I:170-179).
Springer DOI 0601
BibRef

Shekar, B.H., Guru, D.S., Nagabhushan, P.,
Two-Dimensional Optimal Transform for Appearance Based Object Recognition,
ICCVGIP06(650-661).
Springer DOI 0612
BibRef

Irpino, A.[Antonio],
'Spaghetti' PCA analysis: An extension of principal components analysis to time dependent interval data,
PRL(27), No. 5, 1 April 2006, pp. 504-513.
Elsevier DOI 0604
Interval data; Time dependent; Oriented intervals BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.], Onwubolu, G.C.[Godfrey C.],
Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification,
PR(39), No. 7, July 2006, pp. 1215-1229.
WWW Link. 0606
Classification accuracy; Total parameter requirement; Processing time; Class-dependent PCA; LDA BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.],
Subspace independent component analysis using vector kurtosis,
PR(39), No. 11, November 2006, pp. 2227-2232.
WWW Link. 0608
Blind source separation; Subspace ICA; Vector kurtosis BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.],
Fast principal component analysis using fixed-point algorithm,
PRL(28), No. 10, 15 July 2007, pp. 1151-1155.
WWW Link. 0706
Fast PCA; Eigenvalue decomposition; Mean squared error BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.],
A two-stage linear discriminant analysis for face-recognition,
PRL(33), No. 9, 1 July 2012, pp. 1157-1162.
Elsevier DOI 1202
Two-stage linear discriminant analysis; Small sample size problem; Classification accuracy See also new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices, A. BibRef

Vaswani, N., Chellappa, R.,
Principal Components Null Space Analysis for Image and Video Classification,
IP(15), No. 7, July 2006, pp. 1816-1830.
IEEE DOI 0606
BibRef
Earlier:
Classification probability analysis of principal component null space analysis,
ICPR04(I: 240-243).
IEEE DOI 0409
BibRef

Wu, F.C., Hu, Z.Y.,
The LLE and a linear mapping,
PR(39), No. 9, September 2006, pp. 1799-1804.
WWW Link. 0606
Locally linear embedding (LLE); Linear mapping; Principal component analysis BibRef

Tao, Q.[Qing], Wu, G.W.[Gao-Wei], Wang, J.[Jue],
Learning linear PCA with convex semi-definite programming,
PR(40), No. 10, October 2007, pp. 2633-2640.
WWW Link. 0707
Principal component analysis; Statistical learning theory; Support vector machines; Margin; Maximal margin algorithm; Semi-definite programming; Robustness BibRef

Tzimiropoulos, G., Mitianoudis, N., Stathaki, T.,
Robust Recognition of Planar Shapes Under Affine Transforms Using Principal Component Analysis,
SPLetters(14), No. 10, October 2007, pp. 723-726.
IEEE DOI 0711
BibRef

Kumar, K.V.[Kadappagari Vijaya], Negi, A.[Atul],
SubXPCA and a generalized feature partitioning approach to principal component analysis,
PR(41), No. 4, April 2008, pp. 1398-1409.
WWW Link. 0801
Dimensionality reduction; Principal component analysis; Sub-pattern based PCA; Feature partitioning BibRef

Kumar, K.V.[Kadappagari Vijaya], Negi, A.[Atul],
Novel approaches to principal component analysis of image data based on feature partitioning framework,
PRL(29), No. 3, 1 February 2008, pp. 254-264.
WWW Link. 0801
Dimensionality reduction; PCA; Image principal component analysis; Feature partitioning; Face recognition BibRef

Dambreville, S.[Samuel], Rathi, Y.[Yogesh], Tannenbaum, A.[Allen],
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors,
PAMI(30), No. 8, August 2008, pp. 1385-1399.
IEEE DOI 0806
BibRef
Earlier:
Shape-Based Approach to Robust Image Segmentation using Kernel PCA,
CVPR06(I: 977-984).
IEEE DOI 0606
BibRef
And:
A Shape-Based Approach to Robust Image Segmentation,
ICIAR06(I: 173-183).
Springer DOI 0610
BibRef
And: A2, A1, A3:
Comparative Analysis of Kernel Methods for Statistical Shape Learning,
CVAMIA06(96-107).
Springer DOI 0605
BibRef

Majumdar, A.,
Image compression by sparse PCA coding in curvelet domain,
SIViP(3), No. 1, January 2009, pp. xx-yy.
Springer DOI 0902
BibRef

Majumdar, A.[Angshul], Ward, R.K.[Rabab K.],
Improved Group Sparse Classifier,
PRL(31), No. 13, 1 October 2010, pp. 1959-1964.
Elsevier DOI 1003
Classification; Quasi-convex optimization; Face recognition; Character recognition BibRef

Urdiales García, C.[Cristina], Dominguez, M., de Trazegnies, C., Sandoval Hernández, F.[Francisco],
A new pyramid-based color image representation for visual localization,
IVC(28), No. 1, Januray 2010, pp. 78-91.
Elsevier DOI 1001
Localization; Color histogram; Principal components; Hierarchical segmentation; Spatial graph BibRef

Wang, H.X.[Hai-Xian],
Structural two-dimensional principal component analysis for image recognition,
MVA(22), No. 2, March 2011, pp. 433-438.
WWW Link. 1103
BibRef
Earlier:
A new feature extraction method for image recognition using structural two-dimensional locality preserving projections,
ICIP09(2037-2040).
IEEE DOI 0911
BibRef

Wang, H.X.[Hai-Xian],
Block principal component analysis with L1-norm for image analysis,
PRL(33), No. 5, 1 April 2012, pp. 537-542.
Elsevier DOI 1202
PCA; 2DPCA; BPCA; L1-norm; Outlier BibRef

Barshan, E.[Elnaz], Ghodsi, A.[Ali], Azimifar, Z.[Zohreh], Jahromi, M.Z.[Mansoor Zolghadri],
Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds,
PR(44), No. 7, July 2011, pp. 1357-1371.
Elsevier DOI 1103
Dimensionality reduction; Principal component analysis (PCA); Kernel methods; Supervised learning; Visualization; Classification; Regression BibRef

He, R.[Ran], Hu, B.G.[Bao-Gang], Zheng, W.S.[Wei-Shi], Kong, X.W.,
Robust Principal Component Analysis Based on Maximum Correntropy Criterion,
IP(20), No. 6, June 2011, pp. 1485-1494.
IEEE DOI 1106
BibRef

He, R.[Ran], Zheng, W.S.[Wei-Shi], Hu, B.G.[Bao-Gang],
Maximum Correntropy Criterion for Robust Face Recognition,
PAMI(33), No. 8, August 2011, pp. 1561-1576.
IEEE DOI 1107
compute sparse representation of face images for recogntion. correntropy more insensitive to outliers. BibRef

Shao, J.[Jian], Wu, F.[Fei], Ouyang, C.[Chuanfei], Zhang, X.[Xiao],
Sparse spectral hashing,
PRL(33), No. 3, 1 February 2012, pp. 271-277.
Elsevier DOI 1201
Semantic hashing; Sparse principal component analysis; Laplacian eigenmap; AdaBoost BibRef

Li, J.[Jun], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
A probabilistic model for image representation via multiple patterns,
PR(45), No. 11, November 2012, pp. 4044-4053.
Elsevier DOI 1206
Principal component analysis; Probabilistic model BibRef

Bao, B.K., Liu, G., Xu, C., Yan, S.C.,
Inductive Robust Principal Component Analysis,
IP(21), No. 8, August 2012, pp. 3794-3800.
IEEE DOI 1208
BibRef

Bao, B.K., Zhu, G., Shen, J., Yan, S.C.[Shui-Cheng],
Robust Image Analysis With Sparse Representation on Quantized Visual Features,
IP(22), No. 3, March 2013, pp. 860-871.
IEEE DOI 1302
BibRef

Zhang, Z.[Zhao], Zhang, Y.[Yan], Li, F.Z.[Fan-Zhang], Zhao, M.B.[Ming-Bo], Zhang, L.[Li], Yan, S.C.[Shui-Cheng],
Discriminative sparse flexible manifold embedding with novel graph for robust visual representation and label propagation,
PR(61), No. 1, 2017, pp. 492-510.
Elsevier DOI 1705
Flexible manifold embedding BibRef

Liwicki, S.[Stephan], Tzimiropoulos, G.[Georgios], Zafeiriou, S.P.[Stefanos P.], Pantic, M.[Maja],
Euler Principal Component Analysis,
IJCV(101), No. 3, February 2013, pp. 498-518.
WWW Link. 1303
robust dissimilarity based on Euler representation of complex numbers. BibRef

Panagakis, Y.[Yannis], Nicolaou, M.A., Zafeiriou, S.P.[Stefanos P.], Pantic, M.,
Robust Correlated and Individual Component Analysis,
PAMI(38), No. 8, August 2016, pp. 1665-1678.
IEEE DOI 1608
correlation methods BibRef

Papamakarios, G.[Georgios], Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.],
Generalised Scalable Robust Principal Component Analysis,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Kallas, M.[Maya], Honeine, P.[Paul], Richard, C.[Cédric], Francis, C.[Clovis], Amoud, H.[Hassan],
Non-negativity constraints on the pre-image for pattern recognition with kernel machines,
PR(46), No. 11, November 2013, pp. 3066-3080.
Elsevier DOI 1306
Kernel machines; Machine learning; SVM; Kernel PCA; Pre-image problem; Non-negativity constraints; Nonlinear denoising; Pattern recognition BibRef

Qian, J.J.[Jian-Jun], Yang, J.[Jian], Gao, G.[Guangwei],
Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction,
PR(46), No. 10, October 2013, pp. 2724-2739.
Elsevier DOI 1306
Feature extraction; Principal component analysis (PCA); Biometrics; Image representation; Linear discriminant analysis (LDA) BibRef

Candès, E.J.[Emmanuel J.], Li, X.D.[Xiao-Dong], Ma, Y.[Yi], Wright, J.[John],
Robust principal component analysis?,
JACM(58), No. 3, 2011, 11. 1402
BibRef

Zeng, X.Q.A.[Xue-Qi-Ang], Li, G.Z.[Guo-Zheng],
Incremental partial least squares analysis of big streaming data,
PR(47), No. 11, 2014, pp. 3726-3735.
Elsevier DOI 1407
Feature extraction BibRef

Han, F.[Fang], Liu, H.[Han],
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis,
PAMI(36), No. 10, October 2014, pp. 2016-2032.
IEEE DOI 1410
Gaussian distribution BibRef

Hasanbelliu, E.[Erion], Giraldo, L.S.[Luis Sanchez], Principe, J.C.[Jose C.],
Information Theoretic Shape Matching,
PAMI(36), No. 12, December 2014, pp. 2436-2451.
IEEE DOI 1411
BibRef
Earlier:
A Recursive Online Kernel PCA Algorithm,
ICPR10(169-172).
IEEE DOI 1008
Accuracy BibRef

Vonesch, C.[Cédric], Stauber, F.[Frédéric], Unser, M.[Michael],
Steerable PCA for Rotation-Invariant Image Recognition,
SIIMS(8), No. 3, 2015, pp. 1857-1873.
DOI Link 1511
BibRef

Zhang, Z., Li, F., Zhao, M., Zhang, L., Yan, S.,
Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification,
IP(25), No. 6, June 2016, pp. 2429-2443.
IEEE DOI 1605
image classification BibRef

Zhang, Z., Li, F., Zhao, M., Zhang, L., Yan, S.,
Robust Neighborhood Preserving Projection by Nuclear/L2,1-Norm Regularization for Image Feature Extraction,
IP(26), No. 4, April 2017, pp. 1607-1622.
IEEE DOI 1704
feature extraction BibRef

Hauberg, S.[Soren], Feragen, A.[Aasa], Enficiaud, R., Black, M.J.[Michael J.],
Scalable Robust Principal Component Analysis Using Grassmann Averages,
PAMI(38), No. 11, November 2016, pp. 2298-2311.
IEEE DOI 1610
BibRef
Earlier: A1, A2, A4, Only:
Grassmann Averages for Scalable Robust PCA,
CVPR14(3810-3817)
IEEE DOI 1409
Approximation methods. Grassmann manifold BibRef

Wang, Z.H.[Zhen-Hua], Fan, B.[Bin], Wang, G., Wu, F.C.[Fu-Chao],
Exploring Local and Overall Ordinal Information for Robust Feature Description,
PAMI(38), No. 11, November 2016, pp. 2198-2211.
IEEE DOI 1610
BibRef
Earlier: A1, A2, A4, Only:
Affine Subspace Representation for Feature Description,
ECCV14(VII: 94-108).
Springer DOI 1408
Distortion. Affine distortions due to viewpoint changes. PCA on patches. BibRef

Itoh, H.[Hayato], Imiya, A.[Atsushi], Sakai, T.[Tomoya],
Pattern recognition in multilinear space and its applications: mathematics, computational algorithms and numerical validations,
MVA(27), No. 8, November 2016, pp. 1259-1273.
Springer DOI 1612
BibRef
Earlier:
Low-Dimensional Tensor Principle Component Analysis,
CAIP15(I:715-726).
Springer DOI 1511
See also Dimension Reduction and Construction of Feature Space for Image Pattern Recognition. BibRef

Landa, B.[Boris], Shkolnisky, Y.[Yoel],
Steerable Principal Components for Space-Frequency Localized Images,
SIIMS(10), No. 2, 2017, pp. 508-534.
DOI Link 1708
BibRef

Puggini, L.[Luca], McLoone, S.[Seán],
Forward Selection Component Analysis: Algorithms and Applications,
PAMI(39), No. 12, December 2017, pp. 2395-2408.
IEEE DOI 1711
Input variables, Matching pursuit algorithms, Power capacitors, Principal component analysis, Signal processing algorithms, Unsupervised dimensionality reduction, feature selection, subset, selection BibRef


Itoh, H.[Hayato], Imiya, A.[Atsushi], Sakai, T.[Tomoya],
Analysis of Multilinear Subspaces Based on Geodesic Distance,
CAIP17(I: 384-396).
Springer DOI 1708
BibRef

Wang, Q., Gao, Q.,
Robust 2DPCA and Its Application,
Robust16(1152-1158)
IEEE DOI 1612
BibRef

Pena, R., Bresson, X., Vandergheynst, P.[Pierre],
Source localization on graphs via L_1 recovery and spectral graph theory,
IVMSP16(1-5)
IEEE DOI 1608
Diffusion processes BibRef

Shahid, N., Kalofolias, V., Bresson, X., Bronstein, M., Vandergheynst, P.,
Robust Principal Component Analysis on Graphs,
ICCV15(2812-2820)
IEEE DOI 1602
Benchmark testing BibRef

Zhang, C.[Chao], Heeren, B.[Behrend], Rumpf, M.[Martin], Smith, W.A.P.[William A. P.],
Shell PCA: Statistical Shape Modelling in Shell Space,
ICCV15(1671-1679)
IEEE DOI 1602
Computational modeling. Surfaces with non-zero thickness. Deformation, articulation. BibRef

Hung, T.Y.[Tzu-Yi], Lu, J.W.[Ji-Wen], Tan, Y.P.[Yap-Peng], Gao, S.H.[Sheng-Hua],
Efficient Sparsity Estimation via Marginal-Lasso Coding,
ECCV14(IV: 578-592).
Springer DOI 1408
BibRef

Inoue, K.[Kohei], Hara, K.[Kenji], Urahama, K.[Kiichi],
A Unified View of Two-Dimensional Principal Component Analyses,
SSSPR12(566-574).
Springer DOI 1211
BibRef

Mignon, A.[Alexis], Jurie, F.[Frederic],
PCCA: A new approach for distance learning from sparse pairwise constraints,
CVPR12(2666-2672).
IEEE DOI 1208
BibRef

Ayazoglu, M.[Mustafa], Sznaier, M.[Mario], Camps, O.I.[Octavia I.],
Fast algorithms for structured robust principal component analysis,
CVPR12(1704-1711).
IEEE DOI 1208
BibRef

Lim, K.L.[Kart-Leong], Galoogahi, H.K.,
Shape Classification Using Local and Global Features,
PSIVT10(115-120).
IEEE DOI 1011
Histogram of Oriented Gradient (HOG). PCA for both local and global. BibRef

Duanduan, Y.[Yang], Sluzek, A.[Andrzej],
Performance evaluation of low-dimensional sifts,
ICIP10(2729-2732).
IEEE DOI 1009
simplifications of SIFT, analysis. PCA-SIFT. BibRef

Wang, J.[Jing], Su, G.D.[Guang-Da], Chen, J.S.[Jian-Sheng], Moon, Y.S.[Yiu-Sang],
CPGL: A classification method combining PCA and the Group Lasso method,
ICIP10(4529-4532).
IEEE DOI 1009
BibRef

Hui, K.H.[Kang-Hua], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua],
Globally-Preserving Based Locally Linear Embedding,
ICPR10(531-534).
IEEE DOI 1008
BibRef

Bruneau, P.[Pierrick], Gelgon, M.[Marc], Picarougne, F.[Fabien],
Aggregation of Probabilistic PCA Mixtures with a Variational-Bayes Technique Over Parameters,
ICPR10(702-705).
IEEE DOI 1008
BibRef

Negi, A.[Atul], Kadappa, V.K.[Vijaya Kumar],
SubXPCA versus PCA: A Theoretical Investigation,
ICPR10(4170-4173).
IEEE DOI 1008
BibRef

Sommer, S.[Stefan], Lauze, F.[François], Hauberg, S.[Søren], Nielsen, M.[Mads],
Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations,
ECCV10(VI: 43-56).
Springer DOI 1009
Effect of linearization when using Principal Geodesic Analysis. BibRef

Mu, Y.D.[Ya-Dong], Sun, J.[Ju], Han, T.X.[Tony X.], Cheong, L.F.[Loong-Fah], Yan, S.C.[Shui-Cheng],
Randomized Locality Sensitive Vocabularies for Bag-of-Features Model,
ECCV10(III: 748-761).
Springer DOI 1009
BibRef

Mu, Y.D.[Ya-Dong], Shen, J.L.[Jia-Lie], Yan, S.C.[Shui-Cheng],
Weakly-supervised hashing in kernel space,
CVPR10(3344-3351).
IEEE DOI 1006
BibRef

Ozay, N.[Necmiye], Sznaier, M.[Mario], Lagoa, C.[Constantino], Camps, O.I.[Octavia I.],
GPCA with denoising: A moments-based convex approach,
CVPR10(3209-3216).
IEEE DOI 1006
Generalized Principal Component Analysis BibRef

Inoue, K.[Kohei], Hara, K.[Kenji], Urahama, K.[Kiichi],
Robust multilinear principal component analysis,
ICCV09(591-597).
IEEE DOI 0909
BibRef

Serra, J.[Jean],
Adaptive lattices on the unit sphere. Application to remote sensing,
ICIP09(2257-2260).
IEEE DOI 0911
Study color, luminance and hue distributions. BibRef

Laparra, V.[Valero], Camps-Valls, G.[Gustavo], Malo, J.[Jesus],
PCA Gaussianization for image processing,
ICIP09(3985-3988).
IEEE DOI 0911
See also Recovering wavelet relations using SVM for image denoising. BibRef

Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Kernel PCA-based semantic feature estimation approach for similar image retrieval,
ICIP08(965-968).
IEEE DOI 0810
BibRef

Mei, L.[Lin], Figl, M.[Michael], Darzi, A.[Ara], Rueckert, D.[Daniel], Edwards, P.[Philip],
Sample Sufficiency and PCA Dimension for Statistical Shape Models,
ECCV08(IV: 492-503).
Springer DOI 0810
BibRef

Zhao, D.L.[De-Li], Lin, Z.C.[Zhou-Chen], Tang, X.[Xiaoou],
Laplacian PCA and Its Applications,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Maret, Y., Nikolopoulos, S., Dufaux, F., Ebrahimi, T., Nikolaidis, N.,
A Novel Replica Detection System using Binary Classifiers, R-Trees, and PCA,
ICIP06(925-928).
IEEE DOI 0610
BibRef

Sarkis, M., Dawy, Z., Obermeier, F., Diepold, K.,
Automatic Model-Order Selection for PCA,
ICIP06(933-936).
IEEE DOI 0610
BibRef

Jin, Z.[Zhong], Davoine, F.[Franck], Lou, Z.[Zhen], Yang, J.Y.[Jing-Yu],
A Novel PCA-Based Bayes Classifier and Face Analysis,
ICB06(144-150).
Springer DOI 0601
BibRef

Hosic, S.[Sabina], Hocanin, A.[Aykut], Demirel, H.[Hasan],
Unequal Error Protection Using Convolutional Codes for PCA-Coded Images,
ICIAR05(335-342).
Springer DOI 0509
BibRef

Jin, Z.[Zhong], Davoine, F.[Franck],
Orthogonal ICA representation of images,
ICARCV04(I: 369-374).
IEEE DOI 0412
BibRef

Tanaka, T.,
Generalized subspace rules for on-line PCA and their application in signal and image compression,
ICIP04(III: 1895-1898).
IEEE DOI 0505
BibRef

Romaniuk, B., Guilloux, V., Desvignes, M., Deshayes, M.J.,
Partially observed objects localization with PCA and KPCA models,
Southwest04(80-84).
WWW Link. 0411
BibRef

Ke, Y.[Yan], Sukthankar, R.,
PCA-SIFT: a more distinctive representation for local image descriptors,
CVPR04(II: 506-513).
IEEE DOI 0408
See also Distinctive Image Features from Scale-Invariant Keypoints. BibRef

Meltzer, J.[Jason], Yang, M.H.[Ming-Hsuan], Gupta, R.[Rakesh], Soatto, S.[Stefano],
Multiple View Feature Descriptors from Image Sequences via Kernel Principal Component Analysis,
ECCV04(Vol I: 215-227).
Springer DOI 0405
BibRef

Le Bihan, N., Sangwine, S.J.,
Quaternion principal component analysis of color images,
ICIP03(I: 809-812).
IEEE DOI 0312
BibRef

Zeng, X.Y.[Xiang-Yan], Chen, Y.W.[Yen-Wei], Nakao, Z.,
Image feature representation by the subspace of nonlinear PCA,
ICPR02(II: 228-231).
IEEE DOI 0211
BibRef

Hegazy, D.[Doaa], Denzler, J.[Joachim],
Combining Appearance and Range Based Information for Multi-class Generic Object Recognition,
CIARP09(741-748).
Springer DOI 0911
BibRef
Earlier:
Generic Object Recognition Using Boosted Combined Features,
RobVis08(355-366).
Springer DOI 0802
BibRef

Drexler, C., Mattern, F., Denzler, J.,
Appearance Based Generic Object Modeling and Recognition Using Probabilistic Principal Component Analysis,
DAGM02(100 ff.).
Springer DOI 0303
BibRef

Perantonis, S.J., Petridis, S., Virvilis, V.,
Supervised Principal Component Analysis Using a Smooth Classifier Paradigm,
ICPR00(Vol II: 109-112).
IEEE DOI 0009
BibRef

Surendro, K.[Kridanto], Anzai, Y.[Yuichiro],
Non-rigid object recognition using principal component analysis and geometric hashing,
CAIP97(50-57).
Springer DOI 9709
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Invariants -- ICA, Independent Component Analysis .


Last update:Nov 11, 2017 at 13:31:57