MatchNet: Unifying feature and metric learning for patch-based matching

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 3279-3286

  Han, Xufeng ^a   Leung, Thomas ^b   Jia, Yangqing ^b   Sukthankar, Rahul ^b   Berg, Alexander C ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; COMPUTER VISION; FUNCTIONS;

COMPARISON FUNCTIONS; CONVOLUTIONAL NETWORKS; EXPERIMENTAL REPEATABILITIES; FEATURE COMPUTATION; FEATURE REPRESENTATION; SIMILARITY NETWORK; STATE OF THE ART; STORAGE REQUIREMENTS;

PATTERN RECOGNITION;

EID: 84959255777     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298948     Document Type: Conference Paper

References (37)

1. H. Bay, T. Tuytelaars, and L. Van Gool. SURF: Speeded up robust features. In ECCV, 2006
2. J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah. Signature verification using a "Siamese" time delay neural network. In NIPS, 1994
3. M. Brown, G. Hua, and S. A. J. Winder. Discriminative learning of local image descriptors. IEEE TPAMI, 33(1):43-57, 2011
4. M. Brown and D. Lowe. Automatic panoramic image stitching using invariant features. IJCV, 74(1), 2007
5. S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. In CVPR, 2005
6. D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. In CVPR, 2014
7. J. Heinly, E. Dunn, and J.-M. Frahm. Comparative evaluation of binary features. In ECCV, 2012
8. P. Jain, B. Kulis, J. V. Davis, and I. S. Dhillon. Metric and kernel learning using a linear transformation. Journal of Ma-chine Learning Research, 13:519-547, 2012
9. Y. Jia and T. Darrell. Heavy-tailed distances for gradient based image descriptors. In NIPS, pages 397-405, 2011
10. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. Large-scale video classification with convolutional neural networks. In CVPR, 2014
11. Y. Ke and R. Sukthankar. PCA-SIFT: A more distinctive representation for local image descriptors. In CVPR, 2004
12. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012
13. D. Lowe. Object recognition from local scale-invariant features. In ICCV, 1999
14. J. Matas and O. Chum. Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Com-puting, 22(10), 2004
15. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. TPAMI, 27(10):1615-1630, 2005
16. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. J. V. Gool. A comparison of affine region detectors. IJCV, 65, 2005
17. N. Molton, A. Davison, and I. Reid. Locally planar patch features for real-time structure from motion. In BMVC, 2004
18. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. ORB: An efficient alternative to SIFT or SURF. In ICCV, 2011
19. R. Salakhutdinov and G. E. Hinton. Learning a nonlinear embedding by preserving class neighbourhood structure. In AISTATS, 2007
20. S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In CVPR, 2006
21. G. Shakhnarovich. Learning Task-Specific Similarity. PhD thesis, MIT, 2006
22. K. Simonyan, A. Vedaldi, and A. Zisserman. Learning local feature descriptors using convex optimisation. TPAMI, 2014
23. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos, 2014. arXiv preprint arXiv:1406. 2199
24. N. Snavely, S. M. Seitz, and R. Szeliski. Modeling the world from internet photo collections. International Journal of Computer Vision, 80(2):189-210, 2008
25. C. Strecha, A. A. Bronstein, M. M. Bronstein, and P. Fua. LDAHash: Improved matching with smaller descriptors. TPAMI, 34(1):66-78, 2012
26. A. Toshev and C. Szegedy. DeepPose: Human pose estimation via deep neural networks. In CVPR, 2014
27. T. Trzcinski, C. M. Christoudias, P. Fua, and V. Lepetit. Boosting binary keypoint descriptors. In CVPR, 2013
28. T. Trzcinski, C. M. Christoudias, V. Lepetit, and P. Fua. Learning image descriptors with the boosting-trick. In NIPS, pages 278-286, 2012
29. http://www. csie. ntu. edu. tw/cjlin/liblinear/
30. http://www. cs. ubc. ca/mbrown/patchdata/patchdata. html
31. http://www. vlfeat. org/overview/sift. html
32. J. S. Vitter. Random sampling with a reservoir. ACM Trans. Math. Softw., 11(1):37-57, 1985
33. J. Wang, Y. Song, T. Leung, C. Rosenberg, J. Wang, J. Philbin, B. Chen, and Y. Wu. Learning fine-grained image similarity with deep ranking. In CVPR, 2014
34. Y. Weiss, A. B. Torralba, and R. Fergus. Spectral hashing. In NIPS, 2008
35. S. A. J. Winder, G. Hua, and M. Brown. Picking the best DAISY. In CVPR, 2009
36. B. Yao, G. Bradski, and L. Fei-Fei. A codebook-free and annotation-free approach for fine-grained image categorization. In CVPR, 2012
37. J. Zbontar and Y. LeCun. Computing the stereo matching cost with a convolutional neural network. http://arxiv. org/abs/1409. 4326, 2014.