13.4.1.8 Learning for Principal Components, Eigen Representations

Ohba, K., Ikeuchi, K.,
Detectability, Uniqueness, and Reliability of Eigen Windows for Stable Verification of Partially Occluded Objects,
PAMI(19), No. 9, September 1997, pp. 1043-1047.
IEEE DOI 9710
BibRef
Earlier:
Recognition of the Multi Specularity Objects Using the Eigen-Window,
ICPR96(I: 692-696).
IEEE DOI 9608
BibRef
And: CMU-CS-TR-96-105, February 1996.
PS File. BibRef

Ohba, K.[Kohtaro], Sato, Y.[Yoichi], Ikeuchi, K.[Katsushi],
Appearance-Based Visual Learning and Object Recognition with Illumination Invariance,
MVA(12), No. 4, 2000, pp. 189-196.
Springer DOI 0101
BibRef
Earlier:
Appearance Based Object Recognition with Illumination Invariance,
DARPA97(1229-1236). BibRef

Selinger, A.[Andrea], Nelson, R.C.[Randal C.],
A Perceptual Grouping Hierarchy for Appearance-Based 3D Object Recognition,
CVIU(76), No. 1, October 1999, pp. 83-92.
DOI Link BibRef 9910
Earlier: A2, A1:
Perceptual Grouping Hierarchy for 3D Object Recognition and Representation,
DARPA98(157-163). BibRef

Selinger, A., Nelson, R.C.,
Minimally Supervised Acquisition of 3D Recognition Models from Cluttered Images,
CVPR01(I:213-220).
IEEE DOI 0110
BibRef
And:
Appearance-Based Object Recognition Using Multiple Views,
CVPR01(I:905-911).
IEEE DOI 0110
BibRef
Earlier:
Improving Appearance-based Object Recognition in Cluttered Backgrounds,
ICPR00(Vol I: 46-50).
IEEE DOI 0009
Find the closest one to the initial item, keep merging until above threshold to add to the cluster. Learning for DB indexing. BibRef

Nelson, R.C.[Randal C.], Selinger, A.[Andrea],
Learning 3D Recognition Models for General Objects from Unlabeled Imagery: An Experiment in Intelligent Brute Force,
ICPR00(Vol I: 1-8).
IEEE DOI 0009
BibRef
Earlier:
Experiments on (Intelligent) Brute Force Methods for Appearance-Based Object Recognition,
DARPA97(1197-1206). BibRef

Nelson, R.C.[Randal C.], and Selinger, A.[Andrea],
A Cubist Approach to Object Recognition,
ICCV98(614-621).
IEEE DOI BibRef 9800

Lee, D.D., Seung, H.S.,
Learning the Parts of Objects by non-Negative Matrix Factorization,
Nature(401), 1999, pp. 788-791. Generate a positive, sparse component bases, similar to PCA, but sparse. BibRef 9900

Pope, A.R.[Arthur R.], Lowe, D.G.[David G.],
Probabilistic Models of Appearance for 3-D Object Recognition,
IJCV(40), No. 2, November 2000, pp. 149-167.
DOI Link 0101
BibRef
Earlier:
Learning Appearance Models for Object Recognition,
ORCV96(201). 9611
BibRef
Earlier:
Learning Object Recognition Models from Images,
ICCV93(296-301).
IEEE DOI BibRef
And:
Learning 3D Object Recognition Models from 2D Images,
AAAI-MLCV93(xx). University of British Columbia. BibRef

Zheng, W.S.[Wei-Shi], Lai, J.H.[Jian-Huang], Yuen, P.C.[Pong C.], Li, S.Z.[Stan Z.],
Perturbation LDA: Learning the difference between the class empirical mean and its expectation,
PR(42), No. 5, May 2009, pp. 764-779.
Elsevier DOI 0902
Fisher criterion; Perturbation analysis; Face recognition BibRef

Zheng, W.S.[Wei-Shi], Lai, J.H.[Jian-Huang], Yuen, P.C.[Pong C.],
Weakly Supervised Learning on Pre-image Problem in Kernel Methods,
ICPR06(II: 711-715).
IEEE DOI 0609
BibRef

Zheng, W.S.[Wei-Shi], Lai, J.H.[Jian-Huang],
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis,
ICPR06(II: 456-459).
IEEE DOI 0609
BibRef

Cai, H.P.[Hong-Ping], Mikolajczyk, K.[Krystian], Matas, J.G.[Jiri G.],
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors,
PAMI(33), No. 2, February 2011, pp. 338-352.
IEEE DOI 1101
BibRef
Earlier: BMVC08(xx-yy).
PDF File. 0809
LDP. Deal with requirement of large training set, from simulations of image transformations rather than real correspondence data. BibRef

Mikolajczyk, K.[Krystian], Matas, J.G.[Jiri G.],
Improving Descriptors for Fast Tree Matching by Optimal Linear Projection,
ICCV07(1-8).
IEEE DOI 0710
SIFT BibRef

Ali, K.[Karim], Fleuret, F.[François], Hasler, D.[David], Fua, P.[Pascal],
A Real-Time Deformable Detector,
PAMI(34), No. 2, February 2012, pp. 225-239.
IEEE DOI 1112
BibRef
Earlier:
Joint Pose Estimator and Feature Learning for Object Detection,
ICCV09(1373-1380).
IEEE DOI
PDF File. 0909
Single classifier that can deform for different poses, thus estimates the pose at the same time. BibRef

All, K.[Karim], Hasler, D.[David], Fleuret, F.[Frangois],
FlowBoost: Appearance learning from sparsely annotated video,
CVPR11(1433-1440).
IEEE DOI 1106
exploits temporal consistency for complex AM BibRef

Hughes, J.M., Rockmore, D.N., Wang, Y.[Yang],
Bayesian Learning of Sparse Multiscale Image Representations,
IP(22), No. 12, 2013, pp. 4972-4983.
IEEE DOI 1312
belief networks BibRef

Sun, Y.[Yubao], Liu, Q.S.[Qing-Shan], Tang, J.H.[Jin-Hui], Tao, D.C.[Da-Cheng],
Learning Discriminative Dictionary for Group Sparse Representation,
IP(23), No. 9, September 2014, pp. 3816-3828.
IEEE DOI 1410
dictionaries BibRef

Sprechmann, P., Bronstein, A.M., Sapiro, G.,
Learning Efficient Sparse and Low Rank Models,
PAMI(37), No. 9, September 2015, pp. 1821-1833.
IEEE DOI 1508
Computational modeling BibRef

Yuan, Y.H., Li, J., Li, Y., Gou, J., Qiang, J.,
Learning Unsupervised and Supervised Representations via General Covariance,
SPLetters(28), 2021, pp. 145-149.
IEEE DOI 2101
Covariance matrices, Principal component analysis, Kernel, Training, Task analysis, Training data, representation learning BibRef

Rekavandi, A.M.[Aref Miri], Seghouane, A.K.[Abd-Krim], Evans, R.J.[Robin J.],
Learning Robust and Sparse Principal Components With the alpha-Divergence,
IP(33), 2024, pp. 3441-3455.
IEEE DOI 2406
Principal component analysis, Vectors, Loading, Sparse matrices, Robustness, Covariance matrices, Probabilistic logic, maximum likelihood estimator BibRef

Rekavandi, A.M., Seghouane, A.K.,
Robust Principal Component Analysis Using Alpha Divergence,
ICIP20(6-10)
IEEE DOI 2011
Robustness, Principal component analysis, Covariance matrices, Estimation, Loading, Singular value decomposition, Sparse matrices, singular value decomposition BibRef

Li, Q.Z.[Qing-Zhen], Zhao, J.[Jiufen], Zhu, X.P.[Xiao-Ping],
An Unsupervised Learning Algorithm for Intelligent Image Analysis,
ICARCV06(1-5).
IEEE DOI 0612
Learning Kernel PCA. BibRef

Xuan, G.R.[Guo-Rong], Chai, P.Q.[Pei-Qi], Zhu, X.M.[Xiu-Ming], Yao, Q.M.[Qiu-Ming], Huang, C.[Cong], Shi, Y.Q.[Yun Q.], Fu, D.D.[Dong-Dong],
A Novel Pattern Classification Scheme: Classwise Non-Principal Component Analysis (CNPCA),
ICPR06(III: 320-323).
IEEE DOI 0609
BibRef

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Zhang, L.[Lei], Zhang, B.Y.[Ben-Yu], Zhang, H.J.[Hong-Jiang],
Coupled Kernel-Based Subspace Learning,
CVPR05(I: 645-650).
IEEE DOI 0507
BibRef

Pauli, J.[Josef], Sommer, G.[Gerald],
Ellipsoidal Bias in Learning Appearance-Based Recognition Functions,
WTRCV01(201). 0103
BibRef

Shental, N., Hertz, T., Weinshall, D., Pavel, M.,
Adjustment Learning and Relevant Component Analysis,
ECCV02(IV: 776 ff.).
Springer DOI 0205
BibRef

Eriksen, R.D.[René Dencker], Balslev, I.[Ivar],
Training Space Truncation in Vision-Based Recognition,
VF01(494 ff.).
Springer DOI 0209
BibRef

Guillamet, D.[David], Vitrià, J.[Jordi],
Unsupervised Learning of Part-Based Representations,
CAIP01(700-708).
Springer DOI 0210
BibRef

Hou, X.W., Li, S.Z., Zhang, H.J., Cheng, Q.S.,
Direct Appearance Models,
CVPR01(I:828-833).
IEEE DOI 0110
Use texture directly in the prediction of the shape and position. BibRef

Li, S.Z., Hou, X.W., Zhang, H.J., Cheng, Q.S.,
Learning Spatially Localized, Parts-Based Representation,
CVPR01(I:207-212).
IEEE DOI 0110
Faces. Sparse matrix. ICA.
See also Learning the Parts of Objects by non-Negative Matrix Factorization. BibRef

Shah-Hosseini, H., Safabakhsh, R.,
TAPCA: Time Adaptive Self-organizing Maps for Adaptive Principal Components Analysis,
ICIP01(I: 509-512).
IEEE DOI 0108
BibRef

Herbst, B.M.[Ben M.], Muller, N.[Neil],
Building a Representative Training Set Based on Eigenimages,
ICPR98(Vol II: 1846-1848).
IEEE DOI 9808

See also Use of Eigenpictures for Optical Character Recognition, The. BibRef

Abe, T.[Toru], Nakamura, T.[Tomohiko],
Hierarchical-clustering of Parametric Data with Application to the Parametric Eigenspace Method,
ICIP99(IV:118-122).
IEEE DOI BibRef 9900

Abe, T., Nakamura, T.,
Hierarchical Dictionary Constructing Method for the Parametric Eigenspace Method,
MVA98(xx-yy). BibRef 9800

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Computation and Analysis of Principal Components, Eigen Values, SVD .