13.4.1.2 Other, Kernel Methods, Invariants

Chapter Contents (Back)
Kernel Methods. Object Recognition. See also Other Sparse Coding, Invariants.

Nasios, N.[Nikolaos], Bors, A.G.[Adrian G.],
Kernel-based classification using quantum mechanics,
PR(40), No. 3, March 2007, pp. 875-889.
WWW Link. 0611
Kernel density estimation; Nonparametric modelling; Quantum mechanics; Vector field segmentation BibRef

Subrahmanya, N.[Niranjan], Shin, Y.C.[Yung C.],
Sparse Multiple Kernel Learning for Signal Processing Applications,
PAMI(32), No. 5, May 2010, pp. 788-798.
IEEE DOI 1003
For nonlinear models. BibRef

Pan, B.B.[Bin-Bin], Lai, J.H.[Jian-Huang], Chen, W.S.[Wen-Sheng],
Nonlinear nonnegative matrix factorization based on Mercer kernel construction,
PR(44), No. 10-11, October-November 2011, pp. 2800-2810.
Elsevier DOI 1101
Nonnegative matrix factorization; Mercer kernel; Kernel mapping; Face recognition BibRef

Chen, W.S.[Wen-Sheng], Li, Y.G.[Yu-Gao], Pan, B.B.[Bin-Bin], Chen, B.[Bo],
Incremental learning based on block sparse kernel nonnegative matrix factorization,
ICWAPR16(219-224)
IEEE DOI 1611
Databases BibRef

Mantrach, A.[Amin], van Zeebroeck, N.[Nicolas], Francq, P.[Pascal], Shimbo, M.[Masashi], Bersini, H.[Hugues], Saerens, M.[Marco],
Semi-supervised classification and betweenness computation on large, sparse, directed graphs,
PR(44), No. 6, June 2011, pp. 1212-1224.
Elsevier DOI 1102
Graph mining; Semi-supervised classification; Within-network classification; Betweenness centrality; Graph-based classification; Kernel methods; Kernel on a graph; Large-scale graphs BibRef

Ohkawa, Y.[Yasuhiro], Fukui, K.[Kazuhiro],
Hand-Shape Recognition Using the Distributions of Multi-Viewpoint Image Sets,
IEICE(E95-D), No. 6, June 2012, pp. 1619-1627.
WWW Link. 1206
BibRef

Ohkawa, Y.[Yasuhiro], Suryanto, C.H.[Chendra Hadi], Fukui, K.[Kazuhiro],
Image Set-Based Hand Shape Recognition Using Camera Selection Driven by Multi-class AdaBoosting,
ISVC11(II: 555-566).
Springer DOI 1109
BibRef
Earlier: A1, A3, Only:
Hand Shape Recognition Based on Kernel Orthogonal Mutual Subspace Method,
MVA09(122-).
PDF File. 0905
BibRef

Fukui, K.[Kazuhiro], Stenger, B.[Björn], Yamaguchi, O.[Osamu],
A Framework for 3D Object Recognition Using the Kernel Constrained Mutual Subspace Method,
ACCV06(II:315-324).
Springer DOI 0601
map input into feature space. Project onto kernel subspace. BibRef

Fukui, K.[Kazuhiro], Yamaguchi, O.[Osamu],
The Kernel Orthogonal Mutual Subspace Method and Its Application to 3D Object Recognition,
ACCV07(II: 467-476).
Springer DOI 0711
BibRef
Earlier: A2, A1:
Pattern hashing: Object recognition based on a distributed local appearance model,
ICIP02(III: 329-332).
IEEE DOI 0210
BibRef

Igarashi, Y.[Yosuke], Fukui, K.[Kazuhiro],
3D Object Recognition Based on Canonical Angles between Shape Subspaces,
ACCV10(IV: 580-591).
Springer DOI 1011
BibRef

Akihiro, N.[Naoki], Fukui, K.[Kazuhiro],
Compound Mutual Subspace Method for 3D Object Recognition: A Theoretical Extension of Mutual Subspace Method,
Subspace10(374-383).
Springer DOI 1109
BibRef

Liao, C.T.[Chia-Te], Lai, S.H.[Shang-Hong],
Robust kernel-based learning for image-related problems,
IET-IPR(6), No. 6, 2012, pp. 795-803.
DOI Link 1210
BibRef
Earlier:
A novel robust kernel for appearance-based learning,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Kuang, J.J.[Jin-Jun], Chai, Y.[Yi],
Robust Scene Categorization via Scale-Rotation Invariant Generative Model and Kernel Sparse Representation Classification,
IEICE(E96-D), No. 3, March 2013, pp. 758-761.
WWW Link. 1303
BibRef

Sun, T.[Tao], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Wang, S.[Shuang], Feng, J.[Jie],
Selective multiple kernel learning for classification with ensemble strategy,
PR(46), No. 11, November 2013, pp. 3081-3090.
Elsevier DOI 1306
Ensemble learning; Kernel evaluation; Multiple kernel learning; Selective multiple kernel learning; Fast selective multiple kernel learning BibRef

Guo, L.H.[Li-Hua],
Locality-Constrained Multi-Task Joint Sparse Representation for Image Classification,
IEICE(E96-D), No. 9, September 2013, pp. 2177-2181.
WWW Link. 1309
BibRef

Guo, L.H.[Li-Hua],
Manifold Kernel Metric Learning for Larger-Scale Image Annotation,
IEICE(E98-D), No. 7, July 2015, pp. 1396-1400.
WWW Link. 1508
BibRef

Lampert, C.H.[Christoph H.],
Kernel Methods in Computer Vision,
FTCGV(4), Issue 3, 2008, pp. 193-285.
DOI Link 1410
Published September 2009. BibRef

Liu, W.Y.[Wei-Yang], Yu, Z.D.[Zhi-Ding], Lu, L.J.[Li-Jia], Wen, Y.D.[Yan-Dong], Li, H.[Hui], Zou, Y.X.[Yue-Xian],
KCRC-LCD: Discriminative kernel collaborative representation with locality constrained dictionary for visual categorization,
PR(48), No. 10, 2015, pp. 3076-3092.
Elsevier DOI 1507
Kernel collaborative representation BibRef

Liu, W.Y.[Wei-Yang], Yu, Z.D.[Zhi-Ding], Wen, Y.D.[Yan-Dong], Yang, M.[Meng], Zou, Y.X.[Yue-Xian],
Multi-kernel collaborative representation for image classification,
ICIP15(21-25)
IEEE DOI 1512
Collaborative Representation; Image Classification; Multi-Kernel BibRef

Liu, W.Y.[Wei-Yang], Lu, L.[Lijia], Li, H.[Hui], Wang, W.[Wei], Zou, Y.X.[Yue-Xian],
A novel kernel collaborative representation approach for image classification,
ICIP14(4241-4245)
IEEE DOI 1502
Accuracy BibRef

Li, X.[Xiao], Fang, M.[Min], Wang, H.C.[Hong-Chun], Zhang, J.J.[Ju-Jie],
Supervised transfer kernel sparse coding for image classification,
PRL(68, Part 1), No. 1, 2015, pp. 27-33.
Elsevier DOI 1512
Transfer learning BibRef

Li, X.[Xiao], Fang, M.[Min], Zhang, J.J.[Ju-Jie], Wu, J.Q.[Jin-Qiao],
Sample selection for visual domain adaptation via sparse coding,
SP:IC(44), No. 1, 2016, pp. 92-100.
Elsevier DOI 1605
Image classification BibRef

Cinbis, R.G.[Ramazan Gokberk], Verbeek, J.[Jakob], Schmid, C.[Cordelia],
Approximate Fisher Kernels of Non-iid Image Models for Image Categorization,
PAMI(38), No. 6, June 2016, pp. 1084-1098.
IEEE DOI 1605
BibRef
Earlier:
Image categorization using Fisher kernels of non-iid image models,
CVPR12(2184-2191).
IEEE DOI 1208
Analytical models BibRef

Marukatat, S.[Sanparith],
Kernel matrix decomposition via empirical kernel map,
PRL(77), No. 1, 2016, pp. 50-57.
Elsevier DOI 1606
Kernel PCA BibRef

Ji, R.R.[Rong-Rong], Cao, L.J.[Liu-Juan], Wang, Y.[Yan],
Joint Depth and Semantic Inference from a Single Image via Elastic Conditional Random Field,
PR(59), No. 1, 2016, pp. 268-281.
Elsevier DOI 1609
Depth estimation BibRef

Wang, Y.[Yan], Ji, R.R.[Rong-Rong], Chang, S.F.[Shih-Fu],
Label Propagation from ImageNet to 3D Point Clouds,
CVPR13(3135-3142)
IEEE DOI 1309
BibRef

Liu, W.[Wei], Wang, J.[Jun], Ji, R.R.[Rong-Rong], Jiang, Y.G.[Yu-Gang], Chang, S.F.[Shih-Fu],
Supervised hashing with kernels,
CVPR12(2074-2081).
IEEE DOI 1208
BibRef

Zhang, G., Sun, H., Xia, G., Sun, Q.,
Multiple Kernel Sparse Representation-Based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension,
IP(25), No. 9, September 2016, pp. 4271-4285.
IEEE DOI 1609
image classification BibRef

Wang, B.[Bo], Guo, J.C.[Ji-Chang], Zhang, Y.[Yan], Li, C.Y.[Chong-Yi],
Hierarchical feature concatenation-based kernel sparse representations for image categorization,
VC(33), No. 5, May 2017, pp. 647-663.
Springer DOI 1704
BibRef

Lu, Y., Yuan, C., Lai, Z., Li, X., Wong, W.K., Zhang, D.,
Nuclear Norm-Based 2DLPP for Image Classification,
MultMed(19), No. 11, November 2017, pp. 2391-2403.
IEEE DOI 1710
Two-dimensional locality preserving projections. Face recognition, Feature extraction, Image reconstruction, Manifolds, Principal component analysis, Robustness, BibRef


Souilem, N., Elaissi, I., Taouali, O., Hassani, M.,
Identification of non linear system modeled in Reproducing Kernel Hilbert Space using a new criterion,
ICCVIA15(1-6)
IEEE DOI 1603
BibRef
And: ICCVIA15(1-6)
IEEE DOI 1603
Hilbert spaces Biological system modeling BibRef

Shen, B.[Bin], Xu, Z.L.[Zeng-Lin], Allebach, J.P.[Jan P.],
Kernel tapering: A simple and effective approach to sparse kernels for image processing,
ICIP14(4917-4921)
IEEE DOI 1502
Accuracy BibRef

Hartley, R.I.[Richard I.],
Keynote lecture 2: 'Riemannian manifolds, kernels and learning',
AVSS14(XV-XV)
IEEE DOI 1411
Computational modeling BibRef

Aljundi, R.[Rahaf], Emonet, R.[Remi], Muselet, D.[Damien], Sebban, M.[Marc],
Landmarks-based kernelized subspace alignment for unsupervised domain adaptation,
CVPR15(56-63)
IEEE DOI 1510
BibRef

Fernando, B.[Basura], Habrard, A.[Amaury], Sebban, M.[Marc], Tuytelaars, T.[Tinne],
Unsupervised Visual Domain Adaptation Using Subspace Alignment,
ICCV13(2960-2967)
IEEE DOI 1403
domain adaptation; object recognition; subspace alignment Map one subspace to the other. BibRef

Li, P.H.[Pei-Hua], Wang, Q.L.[Qi-Long], Zuo, W.M.[Wang-Meng], Zhang, L.[Lei],
Log-Euclidean Kernels for Sparse Representation and Dictionary Learning,
ICCV13(1601-1608)
IEEE DOI 1403
Dictionary Learning BibRef

Xie, B.[Bojun], Liu, Y.[Yi], Zhang, H.[Hui], Yu, J.[Jian],
Efficient kernel descriptor for image categorization via pivots selection,
ICIP13(3479-3483)
IEEE DOI 1402
Efficient kernel descriptor BibRef

Pan, B.[Binbin], Chen, W.S.[Wen-Sheng],
Learning Geometry-Aware Kernels in a Regularization Framework,
CAIP13(352-359).
Springer DOI 1308
BibRef

Wang, B.[Botao], Xiong, H.K.[Hong-Kai], Jiang, X.Q.[Xiao-Qian], Ling, F.[Fan],
Semi-supervised object recognition using structure kernel,
ICIP12(2157-2160).
IEEE DOI 1302
BibRef

Antanas, L.[Laura], Hoffmann, M., Frasconi, P.[Paolo], Tuytelaars, T.[Tinne], de Raedt, L.[Luc],
A relational kernel-based approach to scene classification,
WACV13(133-139).
IEEE DOI 1303
BibRef

Antanas, L.[Laura], Frasconi, P.[Paolo], Costa, F.[Fabrizio], Tuytelaars, T.[Tinne], de Raedt, L.[Luc],
A Relational Kernel-Based Framework for Hierarchical Image Understanding,
SSSPR12(171-180).
Springer DOI 1211
BibRef

Poczos, B.[Barnabas], Xiong, L.[Liang], Sutherland, D.J.[Dougal J.], Schneider, J.[Jeff],
Nonparametric kernel estimators for image classification,
CVPR12(2989-2996).
IEEE DOI 1208
BibRef

Gavves, E.[Efstratios], Snoek, C.G.M.[Cees G.M.], Smeulders, A.W.M.[Arnold W.M.],
Convex reduction of high-dimensional kernels for visual classification,
CVPR12(3610-3617).
IEEE DOI 1208
BibRef

Zhang, N.[Ning], Farrell, R.[Ryan], Darrell, T.J.[Trever J.],
Pose pooling kernels for sub-category recognition,
CVPR12(3665-3672).
IEEE DOI 1208
BibRef

Li, H.X.[Han-Xi], Gao, Y.S.[Yong-Sheng], Sun, J.[Jun],
Fast Kernel Sparse Representation,
DICTA11(72-77).
IEEE DOI 1205
BibRef

Sakano, H.[Hitoshi], Yamaguchi, O.[Osamu], Kawahara, T.[Tomokazu], Hotta, S.[Seiji],
On the Behavior of Kernel Mutual Subspace Method,
Subspace10(364-373).
Springer DOI 1109
BibRef

Le, T.T.[Tam T.], Kang, Y.[Yousun], Sugimoto, A.[Akihiro], Tran, S.T.[Son T.], Nguyen, T.D.[Thuc D.],
Hierarchical Spatial Matching Kernel for Image Categorization,
ICIAR11(I: 141-151).
Springer DOI 1106
BibRef

Bo, L.F.[Lie-Feng], Lai, K.[Kevin], Ren, X.F.[Xiao-Feng], Fox, D.[Dieter],
Object recognition with hierarchical kernel descriptors,
CVPR11(1729-1736).
IEEE DOI 1106
Kernel descriptors turn pixels into patch features, then with SVM. BibRef

Huang, D.[Dong], Tian, Y.D.[Yuan-Dong], de la Torre, F.[Fernando],
Local isomorphism to solve the pre-image problem in kernel methods,
CVPR11(2761-2768).
IEEE DOI 1106
BibRef

Mukherjee, L.[Lopamudra], Peng, J.M.[Ji-Ming], Sigmund, T.[Trevor], Singh, V.[Vikas],
Network Flow Formulations for Learning Binary Hashing,
ECCV16(V: 366-381).
Springer DOI 1611
BibRef

Mukherjee, L.[Lopamudra], Singh, V.[Vikas], Peng, J.M.[Ji-Ming], Hinrichs, C.[Chris],
Learning kernels for variants of normalized cuts: Convex relaxations and applications,
CVPR10(3145-3152).
IEEE DOI 1006
BibRef

Zeng, Z.[Zhi], Li, H.P.[He-Ping], Liang, W.[Wei], Zhang, S.W.[Shu-Wu],
Similarity-based image classification via kernelized sparse representation,
ICIP10(277-280).
IEEE DOI 1009
BibRef

Li, F.X.[Fu-Xin], Ionescu, C.[Catalin], Sminchisescu, C.[Cristian],
Random Fourier Approximations for Skewed Multiplicative Histogram Kernels,
DAGM10(262-271).
Springer DOI 1009
BibRef

Yakhnenko, O.[Oksana], Honavar, V.[Vasant],
Multiple label prediction for image annotation with multiple Kernel correlation models,
VCL-ViSU09(8-15).
IEEE DOI 0906
To correlate text keywords with image. Uses captions. See also Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. BibRef

Haasdonk, B., Vossen, A., Burkhardt, H.,
Invariance in Kernel Methods by Haar-Integration Kernels,
SCIA05(841-851).
Springer DOI 0506
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Active Appearance Models: AAM .


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