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Elsevier DOI
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IEEE DOI
0102
When the training set is small, PCA (Principal Components Analysis)
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Applied to faces with occlusions.
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Ramamoorthi, R.[Ravi],
Analytic PCA Construction for Theoretical Analysis of Lighting
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PAMI(24), No. 10, October 2002, pp. 1322-1333.
IEEE Abstract.
0210
Analysis of the process by changing lighting.
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Guillamet, D.,
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Evaluation of distance metrics for recognition based on non-negative
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Determining a suitable metric when using non-negative matrix
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ICPR02(II: 128-131).
IEEE DOI
0211
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Guillamet, D.,
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Schiele, B.,
Introducing a weighted non-negative matrix factorization for image
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PRL(24), No. 14, October 2003, pp. 2447-2454.
Elsevier DOI
0307
BibRef
Earlier: A1, A3, A2:
Analyzing non-negative matrix factorization for image classification,
ICPR02(II: 116-119).
IEEE DOI
0211
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Guillamet, D.,
Vitria, J.,
Discriminant basis for object classification,
CIAP01(256-261).
IEEE DOI
0210
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Guillamet, D.[David],
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A Weighted Non-negative Matrix Factorization for Local Representations,
CVPR01(I:942-947).
IEEE DOI
0110
Deal with problems of the original formulation to get better
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Bressan, M.[Marco],
Guillamet, D.[David], and
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Using an ICA Representation of High Dimensional Data for Object
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CVPR01(I:1004-1009).
IEEE DOI
0110
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Bressan, M.[Marco],
Vitriŕ, J.[Jordi],
Independent Modes of Variation in Point Distribution Models,
VF01(123 ff.).
Springer DOI
0209
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Guillamet, D.,
Moghaddam, B.,
Vitria, J.,
Higher-order dependencies in local appearance models,
ICIP03(I: 213-216).
IEEE DOI
0312
BibRef
Earlier: A2, A1, A3:
Local appearance-based models using high-order statistics of image
features,
CVPR03(I: 729-735).
IEEE DOI
0307
BibRef
Wang, L.W.[Li-Wei],
Wang, X.[Xiao],
Zhang, X.R.[Xue-Rong],
Feng, J.F.[Ju-Fu],
The equivalence of two-dimensional PCA to line-based PCA,
PRL(26), No. 1, 1 January 2005, pp. 57-60.
Elsevier DOI
0501
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Martínez, A.M.[Aleix M.],
Zhu, M.L.[Man-Li],
Where Are Linear Feature Extraction Methods Applicable?,
PAMI(27), No. 12, December 2005, pp. 1934-1944.
IEEE DOI
0512
Analyze where and why eigen-based linear equations do not work.
When the smallest angle between the ith eigenvector
and the first i eigenvectors is close to zero,
there are problems.
BibRef
Gao, H.[Hui],
Davis, J.W.[James W.],
Why direct LDA is not equivalent to LDA,
PR(39), No. 5, May 2006, pp. 1002-1006.
Elsevier DOI
0604
BibRef
Earlier:
Sampling Representative Examples for Dimensionality Reduction and
Recognition: Bumping LDA,
ECCV06(III: 275-287).
Springer DOI
0608
Linear discriminant analysis; Direct LDA; Small sample size problem
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Chen, P.[Pei],
Suter, D.[David],
An Analysis of Linear Subspace Approaches for Computer Vision and
Pattern Recognition,
IJCV(68), No. 1, June 2006, pp. 83-106.
Springer DOI
0605
Such as PCA or SVD.
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Bethge, M.[Matthias],
Factorial coding of natural images: how effective are linear models in
removing higher-order dependencies?,
JOSA-A(23), No. 6, June 2006, pp. 1253-1268.
WWW Link.
0610
BibRef
Vicente, M.A.[M. Asuncion],
Hoyer, P.O.[Patrik O.],
Hyvarinen, A.[Aapo],
Equivalence of Some Common Linear Feature Extraction Techniques for
Appearance-Based Object Recognition Tasks,
PAMI(29), No. 5, May 2007, pp. 896-900.
IEEE DOI
0704
Contradictory evaluations of PCA vs. ICA.
Whitened PCA may yield identical results to ICA in some cases.
Describe the situations where ICA improves on PCA.
BibRef
Vicente, M.A.[M. Asunción],
Fernández, C.[Cesar],
Reinoso, O.[Oscar],
Payá, L.[Luis],
3D Object Recognition from Appearance: PCA Versus ICA Approaches,
ICIAR04(I: 547-555).
Springer DOI
0409
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Gao, Q.X.[Quan-Xue],
Is two-dimensional PCA equivalent to a special case of modular PCA?,
PRL(28), No. 10, 15 July 2007, pp. 1250-1251.
Elsevier DOI
0706
Modular PCA; Two-dimensional PCA
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Shih, F.Y.[Frank Y.],
Zhang, K.[Kai],
A distance-based separator representation for pattern classification,
IVC(26), No. 5, May 2008, pp. 667-672.
Elsevier DOI
0803
Pattern representation; Classification; Support vector machine; PCA; LDA
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Zheng, W.S.[Wei-Shi],
Lai, J.H.[Jian-Huang],
Li, S.Z.[Stan Z.],
1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis
better than matrix-based?,
PR(41), No. 7, July 2008, pp. 2156-2172.
Elsevier DOI
0804
Fisher's linear discriminant analysis (LDA); Matrix-based representation;
Vector-based representation; Pattern recognition
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Elad, M.[Michael],
Sparse and Redundant Representations
From Theory to Applications in Signal and Image Processing,
Springer2010, ISBN: 978-1-4419-7010-7
WWW Link.
Survey, Invariants.
Buy this book: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
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Elad, M.,
Sparse and Redundant Representation Modeling: What Next?,
SPLetters(19), No. 12, December 2012, pp. 922-928.
IEEE DOI
1212
BibRef
Eriksson, A.P.[Anders P.],
van den Hengel, A.J.[Anton J.],
Efficient Computation of Robust Weighted Low-Rank Matrix Approximations
Using the L_1 Norm,
PAMI(34), No. 9, September 2012, pp. 1681-1690.
IEEE DOI
1208
BibRef
Earlier:
Efficient computation of robust low-rank matrix approximations in the
presence of missing data using the L1 norm,
CVPR10(771-778).
IEEE DOI
1006
Award, CVPR.
BibRef
Chojnacki, W.[Wojciech],
van den Hengel, A.J.[Anton J.],
Brooks, M.J.[Michael J.],
Generalised Principal Component Analysis:
Exploiting Inherent Parameter Constraints,
VISAPP06(217-228).
Springer DOI
0711
BibRef
Oreifej, O.[Omar],
Shah, M.[Mubarak],
Robust Subspace Estimation Using Low-Rank Optimization:
Theory and Applications,
 
|
Du, H.S.[Hai-Shun],
Hu, Q.P.[Qing-Pu],
Jiang, M.[Manman],
Zhang, F.[Fan],
Two-dimensional principal component analysis based on Schatten p-norm
for image feature extraction,
JVCIR(32), No. 1, 2015, pp. 55-62.
Elsevier DOI
1511
Schatten p-norm
BibRef
Du, H.S.[Hai-Shun],
Zhao, Z.L.[Zhao-Long],
Wang, S.[Sheng],
Hu, Q.P.[Qing-Pu],
Two-dimensional discriminant analysis based on Schatten p-norm for
image feature extraction,
JVCIR(45), No. 1, 2017, pp. 87-94.
Elsevier DOI
1704
Schatten p-norm
BibRef
Martín-Clemente, R.[Rubén],
Zarzoso, V.[Vicente],
On the Link Between L1-PCA and ICA,
PAMI(39), No. 3, March 2017, pp. 515-528.
IEEE DOI
1702
Algorithm design and analysis
BibRef
Lisani, J.L.[Jose-Luis],
Morel, J.M.[Jean-Michel],
Exploring Patch Similarity in an Image,
IPOL(11), 2021, pp. 284-316.
DOI Link
2109
Code, Matching. Compare using PCA
See also On lines and planes of closest fit to systems of points in space. or Gaussian mixture model
See also Maximum Likelihood from Incomplete Data via the EM Algorithm.
BibRef
Li, Y.L.[Yong-Lu],
Xu, Y.[Yue],
Xu, X.Y.[Xin-Yu],
Mao, X.H.[Xiao-Han],
Yao, Y.[Yuan],
Liu, S.Q.[Si-Qi],
Lu, C.[Cewu],
Beyond Object Recognition:
A New Benchmark towards Object Concept Learning,
ICCV23(19972-19983)
IEEE DOI Code:
WWW Link.
2401
BibRef
Chung, H.[Hyunhee],
Park, K.H.[Kyung Ho],
Seo, T.[Taewon],
Cho, S.[Sungwoo],
Phantom of Benchmark Dataset: Resolving Label Ambiguity Problem on
Image Recognition in the Wild,
Novelty23(1-10)
IEEE DOI
2302
Training, Deep learning, Image recognition, Image resolution,
Conferences, Semantics, Neural networks
BibRef
Song, Y.[Yue],
Sebe, N.[Nicu],
Wang, W.[Wei],
Why Approximate Matrix Square Root Outperforms Accurate SVD in Global
Covariance Pooling?,
ICCV21(1095-1103)
IEEE DOI
2203
Training, Backpropagation, Protocols, Computational modeling,
Boosting, Matrix decomposition, Recognition and classification,
Optimization and learning methods
BibRef
Sidibé, D.,
Rastgoo, M.,
Mériaudeau, F.,
On spatio-temporal saliency detection in videos using multilinear PCA,
ICPR16(1876-1880)
IEEE DOI
1705
Feature extraction, Image color analysis,
Principal component analysis, Tensile stress,
Videos, Visualization
BibRef
Hsu, G.S.[Gee-Sern],
Loc, T.T.[Truong Tan],
Chung, S.L.[Sheng-Lun],
A comparison study on appearance-based object recognition,
ICPR12(3500-3503).
WWW Link.
1302
BibRef
Qi, H.C.[Han-Chao],
Hughes, S.M.[Shannon M.],
Invariance of principal components under low-dimensional random
projection of the data,
ICIP12(937-940).
IEEE DOI
1302
BibRef
Sakano, H.[Hitoshi],
A Brief History of the Subspace Methods,
Subspace10(434-435).
Springer DOI
1109
BibRef
Garg, R.[Rahul],
Du, H.[Hao],
Seitz, S.M.[Steven M.],
Snavely, N.[Noah],
The dimensionality of scene appearance,
ICCV09(1917-1924).
IEEE DOI
0909
Analysis of assumptions of PCA type representations.
BibRef
Salgian, A.S.[Andrea Selinger],
Combining local descriptors for 3D object recognition and
categorization,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Earlier:
Using Multiple Patches for 3D Object Recognition,
BP07(1-6).
IEEE DOI
0706
BibRef
Earlier:
Object Recognition Using Local Descriptors: A Comparison,
ISVC06(II: 709-717).
Springer DOI
0611
Build on:
See also Scale and Affine Invariant Interest Point Detectors. SIFT (
See also Distinctive Image Features from Scale-Invariant Keypoints. ),
PCA-SIFT (
See also PCA-SIFT: a more distinctive representation for local image descriptors. )
and keyed context patches (
See also Perceptual Grouping Hierarchy for Appearance-Based 3D Object Recognition, A. ).
BibRef
Choksuriwong, A.,
Laurent, H.,
Emile, B.,
Comparison of Invariant Descriptors for Object Recognition,
ICIP05(I: 377-380).
IEEE DOI
0512
BibRef
Brand, M.,
From Subspace to Submanifold Methods,
BMVC04(xx-yy).
HTML Version.
0508
BibRef
Lenz, R.,
Bui, T.H.[Thanh Hai],
Recognition of non-negative patterns,
ICPR04(III: 498-501).
IEEE DOI
0409
PCA analysis. Prove that the non-negative vauues are right.
BibRef
Fortuna, J.,
Quick, P.,
Capson, D.W.[David W.],
A comparison of subspace methods for accurate position measurement,
Southwest04(16-20).
IEEE DOI
0411
BibRef
Fortuna, J.,
Schuurman, D.C.,
Capson, D.W.[David W.],
A comparison of PCA and ICA for object recognition under varying
illumination,
ICPR02(III: 11-15).
IEEE DOI
0211
BibRef
Fortuna, J.[Jeff],
Capson, D.W.[David W.],
Improved support vector classification using PCA and ICA feature space
modification,
PR(37), No. 6, June 2004, pp. 1117-1129.
Elsevier DOI
0405
BibRef
And:
ICA filters for lighting invariant face recognition,
ICPR04(I: 334-337).
IEEE DOI
0409
BibRef
Wu, Q.A.,
Liu, Z.,
Xiong, Z.X.,
Wang, Y.,
Chen, T.,
Castleman, K.R.,
On optimal subspaces for appearance-based object recognition,
ICIP02(III: 885-888).
IEEE DOI
0210
BibRef
Pedersen, F.[Finn],
Andersson, L.[Leif], and
Bengtsson, E.[Ewert],
Investigating Preprocessing of Multivariate Images in Combination with
Principal Component Analysis,
SCIA97(xx-yy)
HTML Version.
9705
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Learning for Principal Components, Eigen Representations .