Efficient match kernels between sets of features for visual recognition

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 135-143

  Bo, Liefeng ^a   Sminchisescu, Cristian ^b  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

VECTOR SPACES;

BAG OF WORDS; CALTECH; LARGE DATASETS; LINEAR CLASSIFIERS; LOCAL FEATURE; QUANTISATION; SETS OF FEATURES; VISUAL RECOGNITION; VISUAL WORD; WORD REPRESENTATIONS;

COMPUTER VISION;

EID: 78149331496     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. O. Boiman, E. Shechtman, and M. Irani. In defense of nearest-neighbor based image classification. In CVPR, 2008.
2. M. Cuturi and J. Vert. Semigroup kernels on finite sets. In NIPS, 2004.
3. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin. Liblinear: A library for large linear classification. JMLR, 9:1871-1874, 2008.
4. A. Frome, Y. Singer, and J. Malik. Image retrieval and classification using local distance functions. In NIPS, 2006.
5. K. Grauman and T. Darrell. The pyramid match kernel: discriminative classification with sets of image features. In ICCV, 2005.
6. K. Grauman and T. Darrell. The pyramid match kernel: Efficient learning with sets of features. JMLR, 8:725-760, 2007.
7. D. Haussler. Convolution kernels on discrete structures. Technical report, 1999.
8. Zhang J., Marszalek M., Lazebnik S., and Schmid C. Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV, 73(2):213-238, 2007.
9. P. Jain, B. Kulis, and K. Grauman. Fast image search for learned metrics. In CVPR, 2008.
10. F. Jurie and B. Triggs. Creating efficient codebooks for visual recognition. In ICCV, 2005.
11. K. Kim, M. Franz, and B. Schölkopf. Iterative kernel principal component analysis for image modeling. PAMI, 27(9):1351-1366, 2005.
12. R. Kondor and T. Jebara. A kernel between sets of vectors. In ICML, 2003.
13. A. Kumar and C. Sminchisescu. Support kernel machines for object recognition. In ICCV, 2007.
14. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
15. F. Li, R. Fergus, and P. Perona. One-shot learning of object categories. PAMI, 28(4):594-611, 2006.
16. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60:91-110, 2004.
17. S. Lyu. Mercer kernels for object recognition with local features. In CVPR, 2005.
18. P. Moreno, P. Ho, and N. Vasconcelos. A kullback-leibler divergence based kernel for svm classification in multimedia applications. In NIPS, 2003.
19. J. Mutch and D. Lowe. Multiclass object recognition with sparse, localized features. In CVPR, 2006.
20. M. Parsana, S. Bhattacharya, C. Bhattacharyya, and K. Ramakrishnan. Kernels on attributed pointsets with applications. In NIPS, 2007.
21. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In CVPR, 2008.
22. A. Rahimi and B. Recht. Random features for large-scale kernel machines. In NIPS, 2007.
23. A. Rahimi and B. Recht. Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning. In NIPS, 2008.
24. B. Schölkopf, A. Smola, and K. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
25. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In ICML, pages 807-814. ACM, 2007.
26. A. Shashua and T. Hazan. Algebraic set kernels with application to inference over local image representations. In NIPS, 2004.
27. J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In ICCV, 2003.
28. J. van Gemert, J. Geusebroek, C. Veenman, and A. Smeulders. Kernel codebooks for scene categorization. In ECCV, 2008.
29. L. Wolf and A. Shashua. Learning over sets using kernel principal angles. JMLR, 4:913-931, 2003.
30. K. Yu, W. Xu, and Y. Gong. Deep learning with kernel regularization for visual recognition. In NIPS, 2008.
31. H. Zhang, A. Berg, M. Maire, and J. Malik. Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In CVPR, 2006.