13.4.1.1 Other Sparse Coding, Kernel Methods, Invariants

Pham, T.V.[Thang V.], and Smeulders, A.W.M.[Arnold W.M.],
Sparse Representation for Coarse and Fine Object Recognition,
PAMI(28), No. 4, April 2006, pp. 555-567.
IEEE DOI Link 0604
Object appearence using a dictionary of Gaussian differential basis functions. Adding new objects does not require retraining old objects. BibRef

Nasios, N.[Nikolaos], Bors, A.G.[Adrian G.],
Kernel-based classification using quantum mechanics,
PR(40), No. 3, March 2007, pp. 875-889.
WWW Version. 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 Link 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 Link
WWW Version. 1101
Nonnegative matrix factorization; Mercer kernel; Kernel mapping; Face recognition 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 Link
WWW Version. 1102
Graph mining; Semi-supervised classification; Within-network classification; Betweenness centrality; Graph-based classification; Kernel methods; Kernel on a graph; Large-scale graphs BibRef

Feng, J., Ni, B., Xu, D., Yan, S.,
Histogram Contextualization,
IP(21), No. 2, February 2012, pp. 778-788.
IEEE DOI Link 1201
Histogram loses the order. Technique to incoprorate spatial information. BibRef

Sohn, K.[Kihyuk], Jung, D.Y.[Dae Yon], Lee, H.L.[Hong-Lak], Hero, A.O.[Alfred O.],
Efficient learning of sparse, distributed, convolutional feature representations for object recognition,
ICCV11(2643-2650).
IEEE DOI Link 1201
BibRef

Chiang, C.K.[Chen-Kuo], Duan, C.H.[Chih-Hsueh], Lai, S.H.[Shang-Hong], Chang, S.F.[Shih-Fu],
Learning component-level sparse representation using histogram information for image classification,
ICCV11(1519-1526).
IEEE DOI Link 1201
Image group statistics. Select the dictionary to best reconstruct the data. 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 Link 1109
BibRef

Robles-Kelly, A.[Antonio],
Learning a Gaussian basis for spectra representation aimed at reflectance classification,
OTCBVS11(88-95).
IEEE DOI Link 1106
BibRef

Shi, J.P.[Jian-Ping], Ren, X.[Xiang], Dai, G.[Guang], Wang, J.D.[Jing-Dong], Zhang, Z.H.[Zhi-Hua],
A non-convex relaxation approach to sparse dictionary learning,
CVPR11(1809-1816).
IEEE DOI Link 1106
To learn sparse representation (few words to describe it). Concave approach. BibRef

Zontak, M.[Maria], Irani, M.[Michal],
Internal statistics of a single natural image,
CVPR11(977-984).
IEEE DOI Link 1106
From recurrence of small image patches. Priors for solving problems. BibRef

Cai, D.[Deng], Bao, H.J.[Hu-Jun], He, X.F.[Xiao-Fei],
Sparse concept coding for visual analysis,
CVPR11(2905-2910).
IEEE DOI Link 1106
Sparse Concept Coding, to capture geometric structure more than SVD does. BibRef

Zhang, C.J.[Chun-Jie], Liu, J.[Jing], Tian, Q.[Qi], Xu, C.S.[Chang-Sheng], Lu, H.Q.[Han-Qing], Ma, S.D.[Song-De],
Image classification by non-negative sparse coding, low-rank and sparse decomposition,
CVPR11(1673-1680).
IEEE DOI Link 1106
BibRef

He, R.[Ran], Zheng, W.S.[Wei-Shi], Hu, B.G.[Bao-Gang], Kong, X.W.[Xiang-Wei],
Nonnegative sparse coding for discriminative semi-supervised learning,
CVPR11(2849-2856).
IEEE DOI Link 1106
BibRef

Kulkarni, N.[Naveen], Li, B.X.[Bao-Xin],
Discriminative affine sparse codes for image classification,
CVPR11(1609-1616).
IEEE DOI Link 1106
BibRef

Yu, K.[Kai], Lin, Y.Q.[Yuan-Qing], Lafferty, J.[John],
Learning image representations from the pixel level via hierarchical sparse coding,
CVPR11(1713-1720).
IEEE DOI Link 1106
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 Link 1106
BibRef

Bespalov, D.[Dmitriy], Dahl, A.L.[Anders Lindbjerg], Bai, B.[Bing], Shokoufandeh, A.[Ali],
On Inferring Image Label Information Using Rank Minimization for Supervised Concept Embedding,
SCIA11(103-113).
Springer DOI Link 1105
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 Link 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 Link 1106
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 Link 1006
BibRef

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

Wu, L.[Lina], Luo, S.W.[Si-Wei], Sun, W.[Wei], Zheng, X.[Xiang],
Integrating ILSR to Bag-of-Visual Words Model Based on Sparse Codes of SIFT Features Representations,
ICPR10(4283-4286).
IEEE DOI Link 1008
Implicit local spatial relationship. ILSR Sparse codes of SIFT features. BibRef

Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Ruan, X.[Xiang],
Image recognition by learned linear subspace of combined bag-of-features and low-level features,
ICIP10(1049-1052).
IEEE DOI Link 1009
BibRef
And:
Image Categorization by Learned Nonlinear Subspace of Combined Visual-Words and Low-Level Features,
ICPR10(3037-3040).
IEEE DOI Link 1008
Object and scene classes. BibRef

Zhan, Y.[Yubin], Yin, J.P.[Jian-Ping],
Cluster Preserving Embedding,
ICPR10(621-624).
IEEE DOI Link 1008
BibRef

Przelaskowski, A.[Artur],
The Role of Sparse Data Representation in Semantic Image Understanding,
ICCVG10(I: 69-80).
Springer DOI Link 1009
BibRef

Huang, J.B.[Jia-Bin], Yang, M.H.[Ming-Hsuan],
Fast sparse representation with prototypes,
CVPR10(3618-3625).
IEEE DOI Link 1006
BibRef

Liu, Y.[Yanan], Wu, F.[Fei], Zhang, Z.H.[Zhi-Hua], Zhuang, Y.T.[Yue-Ting], Yan, S.C.[Shui-Cheng],
Sparse representation using nonnegative curds and whey,
CVPR10(3578-3585).
IEEE DOI Link 1006
Set of sparse and nonnegative representations. Then incorporate these into a sparse representation. BibRef

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

Igarashi, Y.[Yosuke], Fukui, K.[Kazuhiro],
3D Object Recognition Based on Canonical Angles between Shape Subspaces,
ACCV10(IV: 580-591).
Springer DOI Link 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 Link 1109
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 Link 1109
BibRef
Earlier: A1, A3, Only:
Hand Shape Recognition Based on Kernel Orthogonal Mutual Subspace Method,
MVA09(122-).
PDF Version. 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 Link 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 Link 0711
BibRef

Yamaguchi, O.[Osamu], Fukui, K.[Kazuhiro],
Pattern hashing: Object recognition based on a distributed local appearance model,
ICIP02(III: 329-332).
IEEE Abstract. 0210
BibRef

Yakhnenko, O.[Oksana], Honavar, V.[Vasant],
Multiple label prediction for image annotation with multiple Kernel correlation models,
VCL-ViSU09(8-15).
IEEE DOI Link 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

Nakashizuka, M.[Makoto], Nishiura, H.[Hidenari], Iiguni, Y.[Youji],
Sparse image representations with shift-invariant tree-structured dictionaries,
ICIP09(2145-2148).
IEEE DOI Link 0911
BibRef

Gong, D.[Dian], Zhao, X.M.[Xue-Mei], Yang, Q.[Qiong],
Sparse Non-negative Pattern Learning for image representation,
ICIP08(981-984).
IEEE DOI Link 0810
Patterns are learned, features are extracted then used for representation. BibRef

Liao, C.T.[Chia-Te], Lai, S.H.[Shang-Hong],
A novel robust kernel for appearance-based learning,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

Tsai, Y.T.[Yun-Ta], Wang, Q.[Quan], You, S.[Suya],
CDIKP: A highly-compact local feature descriptor,
ICPR08(1-4).
IEEE DOI Link 0812
SIFT combined with projection BibRef

Heiler, M.[Matthias], Schnörr, C.[Christoph],
Controlling Sparseness in Non-negative Tensor Factorization,
ECCV06(I: 56-67).
Springer DOI Link 0608
BibRef

Heiler, M.[Matthias], Schnörr, C.[Christoph],
Reverse-Convex Programming for Sparse Image Codes,
EMMCVPR05(600-616).
Springer DOI Link 0601
BibRef
And:
Learning Non-Negative Sparse Image Codes by Convex Programming,
ICCV05(II: 1667-1674).
IEEE DOI Link 0510
Aim to preserve local structure, unlike PCA. See also Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. BibRef

Hazan, T.[Tamir], Hardoon, R.[Roee], Shashua, A.[Amnon],
pLSA for Sparse Arrays With Tsallis Pseudo-Additive Divergence: Noise Robustness and Algorithm,
ICCV07(1-8).
IEEE DOI Link 0710
BibRef

Polak, S.[Simon], Shashua, A.[Amnon],
The Semi-explicit Shape Model for Multi-object Detection and Classification,
ECCV10(II: 336-349).
Springer DOI Link 1009
BibRef

Shashua, A.[Amnon], Zass, R.[Ron], Hazan, T.[Tamir],
Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization,
ECCV06(IV: 595-608).
Springer DOI Link 0608
See also Probabilistic graph and hypergraph matching. BibRef

Hazan, T.[Tamir], Polak, S.[Simon], Shashua, A.[Amnon],
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization,
ICCV05(I: 50-57).
IEEE DOI Link 0510
Generate descriptions (e.g. bases) of images. BibRef

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

Haasdonk, B., Halawani, A., Burkhardt, H.,
Adjustable Invariant Features by Partial Haar-Integration,
ICPR04(II: 769-774).
IEEE DOI Link 0409
BibRef

Molina-Gamez, M., Subirana-Vilanova, J.B.,
Sparse Groups: A Polynomial Middle-Level Approach for Object Recognition,
ICPR96(I: 518-522).
IEEE DOI Link 9608
(Autonomous Univ. of Barcelona, E) BibRef

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