Kernel methods in computer vision

Foundations and Trends in Computer Graphics and Vision

Volumn 4, Issue 3, 2009, Pages 193-285

  Lampert, Christoph H ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

CLASSICAL TECHNIQUES; DIMENSIONALITY REDUCTION; KERNEL METHODS; LEARNING THE KERNEL; OUTLIER DETECTION; STATISTICAL DEPENDENCIES; STRUCTURE DATA;

COMPUTER VISION;

COMPUTATIONAL METHODS;

EID: 70350091440     PISSN: 15722740     EISSN: 15722759     Source Type: Journal    
DOI: 10.1561/0600000027     Document Type: Article

References (112)

1. D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, and A. Ng, "Discriminative learning of markov random fields for segmentation of 3D scan data, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 169-176, 2005.
2. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan, "Multiple kernel learning, conic duality, and the SMO algorithm, " in International Conference on Machine Learning (ICML), 2004.
3. H. S. Baird, "Document image defect models and their uses, " in International Conference on Document Analysis and Recognition (ICDAR), 1993.
4. G. H. Bakir, M. Wu, and J. Eichhorn, "Maximum-margin feature combination for detection and categorization, " Technical Report, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005.
5. H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded up robust features, " in European Conference on Computer Vision (ECCV), pp. 404-417, May 2006.
6. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection, " IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 19, no. 7, pp. 711-720, 1997.
7. M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation, " Neural Computation, vol. 15, no. 6, pp. 1373-1396, 2003.
8. A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik, "Support vector clustering, " Journal of Machine Learning Research (JMLR), vol. 2, pp. 125-137, 2002.
9. K. P. Bennett and E. J. Bredensteiner, "Duality and geometry in SVM classifiers, " in International Conference on Machine Learning (ICML), 2000.
10. C. M. Bishop, Neural Networks for Pattern Recognition. USA: Oxford University Press, 1995.
11. M. B. Blaschko and C. H. Lampert, "Correlational spectral clustering, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
12. M. B. Blaschko and C. H. Lampert, "Learning to localize objects with structured output regression, " in European Conference on Computer Vision (ECCV), 2008.
13. M. B. Blaschko and C. H. Lampert, "Object localization with global and local context kernels, " in British Machine Vision Conference (BMVC), 2009.
14. A. Bosch, A. Zisserman, and X. Munoz, "Representing shape with a spatial pyramid kernel, " in International Conference on Image and Video Retrieval (CIVR), pp. 401-408, 2007.
15. S. P. Boyd and L. Vandenberghe, Convex optimization. Cambridge University Press, 2004.
16. Y. Boykov and V. Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision, " IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 26, no. 9, pp. 1124- 1137, 2004.
17. L. Breiman, Classification and Regression Trees. Chapman & Hall/CRC, 1998.
18. M. B̌adoiu, S. Har-Peled, and P. Indyk, "Approximate clustering via core-sets, " in ACM Symposium on Theory of Computing, pp. 250-257, 2002.
19. O. Chapelle, "Training a support vector machine in the primal, " Neural Computation, vol. 19, no. 5, pp. 1155-1178, 2007.
20. O. Chapelle, "Training a support vector machine in the primal, " Neural Computation, vol. 19, no. 5, pp. 1155-1178, 2007.
21. M. Charikar, S. Khuller, D. Mount, and G. Narasimhan, "Algorithms for facility location problems with outliers, " in ACM SIAM Symposium on Discrete Algorithms (SODA), pp. 642-651, 2001.
22. Y. Chen, X. S. Zhou, and T. S. Huang, "One-class SVM for learning in image retrieval, " in IEEE International Conference on Image Processing (ICIP), pp. 34-37, 2001.
23. K. Crammer and Y. Singer, "On the algorithmic implementation of multiclass kernel-based vector machines, " Journal of Machine Learning Research (JMLR), vol. 2, pp. 265-292, 2001.
24. N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. Kandola, "On kernel-target alignment, " in Advances in Neural Information Processing Systems (NIPS), vol. 15, pp. 367-373, 2001.
25. G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, "Visual categorization with bags of keypoints, " in ECCV Workshop on Statistical Learning in Computer Vision, 2004.
26. D. Decoste and B. Schölkopf, "Training invariant support vector machines, " Machine Learning, vol. 46, no. 1, pp. 161-190, 2002.
27. T. Deselaers, D. Keysers, and H. Ney, "Improving a discriminative approach to object recognition using image patches, " in Symposium of the German Pattern Recognition Society (DAGM), pp. 326-335, 2005.
28. T. G. Dietterich and G. Bakiri, "Solving multiclass learning problems via error-correcting output codes, " Journal of Artificial Intelligence Research, vol. 2, pp. 263-286, 1995.
29. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, "Behavior recognition via sparse spatio-temporal features, " in IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VSPETS), pp. 65-72, 2005.
30. H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support vector regression machines, " in Advances in Neural Information Processing Systems (NIPS), vol. 10, pp. 155-161, 1996.
31. B. Efron and G. Gong, "A leisurely look at the bootstrap, the jackknife, and cross-validation, " American Statistician, vol. 37, no. 1, pp. 36-48, 1983.
32. R. Fergus, P. Perona, and A. Zisserman, "Object class recognition by unsupervised scale-invariant learning, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2003.
33. M. A. Fischler and R. A. Elschlager, "The representation and matching of pictorial structures, " IEEE Transactions on Computers, vol. 100, no. 22, pp. 67- 92, 1973.
34. G. Forman, "An extensive empirical study of feature selection metrics for text classification, " Journal of Machine Learning Research (JMLR), vol. 3, pp. 1289-1305, 2003.
35. K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf, "Kernel measures of conditional dependence, " in Advances in Neural Information Processing Systems (NIPS), vol. 22, pp. 489-496, 2008.
36. P. Gehler and S. Nowozin, "Feature combination methods for kernel classifiers, " in IEEE International Conference on Computer Vision (ICCV), 2009.
37. P. Gehler and S. Nowozin, "Let the kernel figure it out; principled learning of pre-processing for kernel classifiers, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
38. M. Girolami, "Mercer kernel-based clustering in feature space, " IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 780-784, 2002.
39. K. Grauman and T. Darrell, "The pyramid match kernel: Discriminative classification with sets of image features, " in IEEE International Conference on Computer Vision (ICCV), pp. 1458-1465, 2005.
40. D. Gusfield, Algorithms on Strings, Trees, and Sequences. New York: Cambridge University Press, 1997.
41. B. Haasdonk and H. Burkhardt, "Invariant kernel functions for pattern analysis and machine learning, " Machine Learning, vol. 68, no. 1, pp. 35-61, 2007.
42. C. Harris and M. Stephens, "A combined corner and edge detector, " in Alvey Vision Conference, pp. 147-151, 1988.
43. M. Hein and O. Bousquet, "Hilbertian metrics and positive definite kernels on probability measures, " in International Conference on Artificial Intelligence and Statistics (AISTATS), 2005.
44. D. Hiemstra, "A probabilistic justification for using tf×idf term weighting in information retrieval, " International Journal on Digital Libraries, vol. 3, no. 2, pp. 131-139, 2000.
45. G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets, " Neural Computation, vol. 18, no. 7, pp. 1527-1554, 2006.
46. R. Horst and N. V. Thoai, "DC programming: Overview, " Journal of Optimization Theory and Applications, vol. 103, no. 1, pp. 1-43, 1999.
47. H. Hotelling, "Relations between two sets of variates, " Biometrika, vol. 28, no. 3-4, pp. 321-377, 1936.
48. C. Hsu and C. Lin, "A comparison of methods for multiclass support vector machines, " IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415- 425, 2002. (Pubitemid 34475042)
49. Y. G. Jiang, C. W. Ngo, and J. Yang, "Towards optimal bag-of-features for object categorization and semantic video retrieval, " in International Conference on Image and Video Retrieval (CIVR), pp. 494-501, 2007.
50. T. Joachims, "Training linear SVMs in linear time, " in ACM International Conference on Knowledge Discovery and Data Mining (KDD), pp. 217-226, New York, NY, USA: ACM, 2006.
51. I. T. Jolliffe, Principal Component Analysis. New York: Springer, 1986.
52. M. Kloft, U. Brefeld, P. Laskov, and S. Sonnenburg, "Non-sparse multiple kernel learning, " in NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels, 2008.
53. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection, " in International Joint Conference on Artificial Intelligence (IJCAI), pp. 1137-1145, 1995.
54. A. Kumar and C. Sminchisescu, "Support kernel machines for object recognition, " in IEEE International Conference on Computer Vision (ICCV), 2007.
55. J. T. Y. Kwok and I. W. H. Tsang, "The pre-image problem in kernel methods, " IEEE Transactions on Neural Networks, vol. 15, no. 6, pp. 1517-1525, 2004.
56. J. D. Lafferty, A. McCallum, and F. C. N. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data, " in International Conference on Machine Learning (ICML), pp. 282-289, 2001.
57. P. L. Lai and C. Fyfe, "Kernel and nonlinear canonical correlation analysis, " International Journal of Neural Systems, vol. 10, no. 5, pp. 365-378, 2000.
58. C. H. Lampert and M. B. Blaschko, "A multiple kernel learning approach to joint multi-class object detection, " in Symposium of the German Pattern Recognition Society (DAGM), pp. 31-40, 2008.
59. C. H. Lampert, M. B. Blaschko, and T. Hofmann, "Beyond sliding windows: Object localization by efficient subwindow search, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
60. C. H. Lampert, M. B. Blaschko, and T. Hofmann, "Efficient subwindow search: A branch and bound framework for object localization, " IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2009.
61. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, "Learning the kernel matrix with semidefinite programming, " Journal of Machine Learning Research (JMLR), vol. 5, pp. 27-72, 2004.
62. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2169-2178, 2006.
63. Y. Li, S. Gong, J. Sherrah, and H. Liddell, "Support vector machine based multi-view face detection and recognition, " Image and Vision Computing, vol. 22, no. 5, pp. 413-427, 2004.
64. A. Linden and J. Kindermann, "Inversion of multilayer nets, " in International Joint Conference on Neural Networks (IJCNN), pp. 425-430, 1989.
65. E. Loupias, N. Sebe, S. Bres, and J. M. Jolion, "Wavelet-based salient points for image retrieval, " in IEEE International Conference on Image Processing (ICIP), pp. 518-521, 2000.
66. D. G. Lowe, "Distinctive image features from scale-invariant keypoints, " International Journal of Computer Vision (IJCV), vol. 60, no. 2, pp. 91-110, 2004.
67. S. Lyu and H. Farid, "Steganalysis using color wavelet statistics and oneclass support vector machines, " in SPIE Symposium on Electronic Imaging, pp. 35-45, San Jose, CA, 2004.
68. J. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations, " in Fifth Berkeley Symposium on Mathematics, Statistics and Probability, vol. 1, pp. 281-296, 1967.
69. G. J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition. Wiley New York, 1992.
70. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K. R. Müller, "Fisher discriminant analysis with kernels, " in IEEE Workshop on Neural Networks for Signal Processing, pp. 41-48, 1999.
71. K. Mikolajczyk and C. Schmid, "Scale and affine invariant interest point detectors, " International Journal of Computer Vision (IJCV), vol. 60, no. 1, pp. 63-86, 2004.
72. J. L. Mundy and A. Zisserman, Geometric Invariance in Computer Vision. Cambridge, MA, USA: MIT Press, 1992.
73. S. A. Nene, S. K. Nayar, and H. Murase, "Columbia Object Image Library (COIL-100), " Technical Report, Department of Computer Science Columbia University, New York, 1996.
74. E. Nowak, F. Jurie, and B. Triggs, "Sampling strategies for bag-of-features image classification, " in European Conference on Computer Vision (ECCV), pp. 490-503, 2006.
75. S. Nowozin, G. Bakir, and K. Tsuda, "Discriminative subsequence mining for action classification, " in IEEE International Conference on Computer Vision (ICCV), 2007.
76. F. Odone, A. Barla, and A. Verri, "Building kernels from binary strings for image matching, " IEEE Transactions on Image Processing, vol. 14, no. 2, pp. 169-180, 2005.
77. J. C. Platt, "Fast training of support vector machines using sequential minimal optimization, " in Advances in Kernel Methods: Support Vector Learning, pp. 185-208, MIT Press, 1999.
78. J. C. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin DAGs for multiclass classification, " in Advances in Neural Information Processing Systems (NIPS), vol. 13, pp. 547-553, 1999.
79. N. Quadrianto, L. Song, and A. J. Smola, "Kernelized sorting, " in Advances in Neural Information Processing Systems (NIPS), vol. 22, 2008.
80. J. R. Quinlan, "Induction of decision trees, " Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
81. C. E. Rasmussen and C. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.
82. A. Rényi, "On measures of dependence, " Acta Mathematica Hungarica, vol. 10, no. 3, pp. 441-451, 1959.
83. R. Rifkin and A. Klautau, "In defense of one-vs-all classification, " Journal of Machine Learning Research (JMLR), vol. 5, pp. 101-141, 2004.
84. C. Saunders, A. Gammerman, and V. Vovk, "Ridge regression learning algorithm in dual variables, " in International Conference on Machine Learning (ICML), pp. 515-521, 1998.
85. R. Schapire and Y. Freund, "A decision theoretic generalization of on-line learning and an application to boosting, " Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.
86. B. Schölkopf, C. Burges, and V. Vapnik, "Extracting support data for a given task, " in ACM International Conference on Knowledge Discovery and Data Mining (KDD), pp. 252-257, 1995.
87. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high-dimensional distribution, " Neural Computation, vol. 13, no. 7, pp. 1443-1471, 2001.
88. B. Schölkopf and A. J. Smola, Learning with Kernels. MIT Press, 2002.
89. S. Shalev-Shwartz, Y. Singer, and N. Srebro, "Pegasos: Primal estimated sub- GrAdient SOlver for SVM, " in International Conference on Machine Learning (ICML), pp. 807-814, 2007.
90. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
91. J. Sietsma and R. J. F. Dow, "Creating artificial neural networks that generalize, " Neural Networks, vol. 4, no. 1, pp. 67-79, 1991.
92. P. Simard, Y. LeCun, J. S. Denker, and B. Victorri, "Transformation invariance in pattern recognition - Tangent distance and tangent propagation, " in Neural Networks: Tricks of the Trade, pp. 239-274, Springer, 1999. (Pubitemid 128162419)
93. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos, " in IEEE International Conference on Computer Vision, pp. 1470-1477, 2003.
94. J. R. Smith and S. F. Chang, "Tools and techniques for color image retrieval, " in Storage and Retrieval for Image and Video Databases (SPIE), pp. 426-437, 1996.
95. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, "Large scale multiple kernel learning, " The Journal of Machine Learning Research, vol. 7, pp. 1531- 1565, 2006.
96. M. J. Swain and D. H. Ballard, "Color indexing, " International Journal of Computer Vision (IJCV), vol. 7, no. 1, pp. 11-32, 1991.
97. M. Szummer, P. Kohli, and D. Hoiem, "Learning CRFs using graph cuts, " in European Conference on Computer Vision (ECCV), pp. 582-595, 2008.
98. B. Taskar, C. Guestrin, and D. Koller, "Max-margin markov networks, " in Advances in Neural Information Processing Systems (NIPS), vol. 17, 2003.
99. D. M. J. Tax and R. P. W. Duin, "Support vector data description, " Machine Learning, vol. 54, no. 1, pp. 45-66, 2004.
100. M. Tipping and B. Schölkopf, "A kernel approach for vector quantization with guaranteed distortion bounds, " in Artificial Intelligence and Statistics (AISTATS), pp. 129-134, 2001.
101. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables, " Journal of Machine Learning Research (JMLR), vol. 6, pp. 1453-1484, 2005.
102. M. Turk and A. Pentland, "Eigenfaces for recognition, " Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
103. T. Tuytelaars, C. Lampert, M. Blaschko, and W. Buntine, "Unsupervised object discovery: A comparison, " International Journal of Computer Vision (IJCV), 2009.
104. T. Tuytelaars and K. Mikolajczyk, "Local invariant feature detectors: A survey, " Foundations and Trends in Computer Graphics and Vision, vol. 3, no. 3, pp. 177-280, 2008.
105. V. N. Vapnik, Statistical Learning Theory. Wiley, 1998.
106. M. Varma and D. Ray, "Learning the discriminative power-invariance tradeoff, " in IEEE International Conference on Computer Vision (ICCV), 2007.
107. C. Wallraven, B. Caputo, and A. Graf, "Recognition with local features: The kernel recipe, " in IEEE International Conference on Computer Vision (ICCV), pp. 257-264, 2003.
108. J. Weston, A. Elisseeff, B. Schölkopf, and M. Tipping, "Use of the zero norm with linear models and kernel methods, " Journal of Machine Learning Research (JMLR), vol. 3, pp. 1439-1461, 2003.
109. J. Wood, "Invariant pattern recognition: A review, " Pattern Recognition, vol. 29, no. 1, pp. 1-17, 1996.
110. M. Yang, "Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods, " in IEEE International Conference on Automatic Face and Gesture Recognition, pp. 215-220, 2002.
111. R. A. Young, "The Gaussian derivative model for spatial vision: I. Retinal mechanisms, " Spatial Vision, vol. 2, no. 4, pp. 273-293, 1987.
112. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, "Local features and kernels for classification of texture and object categories: A comprehensive study, " International Journal of Computer Vision (IJCV), vol. 73, no. 2, pp. 213-238, 2007.