Learning image descriptors with boosting

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 37, Issue 3, 2015, Pages 597-610

  Trzcinski, Tomasz ^a   Christoudias, Mario ^a   Lepetit, Vincent ^b  

^a EPFL   (Switzerland)

^b GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

binary embedding; boosting; Learning feature descriptors

Indexed keywords

HASH FUNCTIONS;

BINARY EMBEDDING; BOOSTING; FEATURE DESCRIPTORS; FLOATING POINTS; IMAGE DESCRIPTORS; MEMORY FOOTPRINT; NEW APPLICATIONS; STATE OF THE ART;

DIGITAL ARITHMETIC;

EID: 84923059540     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2014.2343961     Document Type: Article

References (45)

1. D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 20, no. 2, pp. 91-110, 2004.
2. th Eur. Conf. Comput. Vis., 2006, pp. 404-417.
3. G. Shakhnarovich, "Learning task-specific similarity," Ph.D. dissertation, Dept. Elect. Eng. Comput. Sci., MIT, Cambridge, MA, 2006.
4. M. Brown, G. Hua, and S. Winder, "Discriminative learning of local image descriptors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 1, pp. 43-57 Jan. 2011.
5. C. Strecha, A. Bronstein, M. Bronstein, and P. Fua, "LDAHash: Improved matching with smaller descriptors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 1, pp. 66-78 Jan. 2012.
6. K. Simonyan, A. Vedaldi, and A. Zisserman, "Descriptor learning using convex optimisation," in Proc. Eur. Conf. Comput. Vis., 2012, pp. 243-256.
7. th Asian Conf. Comput, Vis., 2009, pp. 214-223.
8. P. Jain, B. Kulis, J. Davis, and I. Dhillon, "Metric and kernel learning using a linear transformation," J. Mach. Learn. Res., vol. 13, pp. 519-547 2012.
9. O. Chapelle, P. Shivaswamy, S. Vadrevu, K. Weinberger, Y. Zhang, and B. Tseng, "Boosted multi-task learning," Mach. Learn., vol. 85, pp. 149-173, 2010.
10. E. Rublee, V. Rabaud, K. Konolidge, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 2564-2571.
11. Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, "Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 12, pp. 2916-2929, Dec. 2013.
12. M. Calonder, V. Lepetit, M. Ozuysal, T. Trzcinski, C. Strecha, and P. Fua, "BRIEF: Computing a local binary descriptor very fast," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 7, pp. 1281-1298, Jul. 2012.
13. S. Leutenegger, M. Chli, and R. Siegwart, "BRISK: Binary robust invariant scalable keypoints," in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 2548-2555.
14. th Eur. Conf. Comput. Vis., 2012, pp. 228-242.
15. M. Norouzi, A. Punjani, and D. Fleet, "Fast search hamming space with multi-index hashing," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2012, pp. 3108-3115.
16. M. Malekesmaeili, R. Ward, and M. Fatourechi, "A fast approximate nearest neighbor search algorithm the Hamming space," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 12, pp. 2481-2488, Dec. 2012.
17. T. Trzcinski, M. Christoudias, V. Lepetit, and P. Fua, "Learning image descriptors with the boosting-trick," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 278-286.
18. T. Trzcinski, M. Christoudias, P. Fua, and V. Lepetit, "Boosting binary keypoint descriptors," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2013, pp. 2874-2881.
19. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, "A comparison of affine region detectors," Int. J. Comput. Vis., vol. 65, no. 1/2, pp. 43-72, 2005.
20. D. Nister and H. Stewenius, "Scalable recognition with a vocabulary tree," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2006, pp. 2161-2168.
21. V. Chandrasekhar, G. Takacs, D. Chen, S. Tsai, R. Grzeszczuk, and B. Girod, "CHoG: Compressed histogram of gradients a low bitrate feature descriptor," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 2504-2511, 2009.
22. H. Jégou, M. Douze, C. Schmid, and P. Pérez, "Aggregating local descriptors into a compact image representation," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 3304-3311.
23. th Eur. Conf. Comput. Vis., 2010, pp. 170-182.
24. B. Kulis and T. Darrell, "Learning to hash with binary reconstructive embeddings," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1042-1050.
25. R. Salakhutdinov and G. Hinton, "Semantic hashing," Int. J. Approximate Reasoning, vol. 50, no. 7, pp. 969-978, 2009.
26. Y. Weiss, A. Torralba, and R. Fergus, "Spectral hashing," in Proc. Adv. Neural Inf. Process. Syst., vol. 21, 2009, pp. 1753-1760.
27. th Eur. Conf. Comput. Vis., 2012, pp. 340-353.
28. M. Norouzi and D. Fleet, "Minimal loss hashing for compact binary codes," in Proc. Int. Conf. Mach. Learn., 2011, pp. 353-360.
29. J. Wang, S. Kumar, and S.-F. Chang, "Sequential projection learning for hashing with compact codes," in Proc. Int. Conf. Mach. Learn., 2010, pp. 1127-1134.
30. W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang, "Supervised hashing with kernels," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2012, pp. 2074-2081.
31. K. Grauman and T. Darrell, "The pyramid match kernel: Discriminative classification with sets of image features," in Proc. IEEE Int. Conf. Comput. Vis., 2005, pp. 1458-1465.
32. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Eur. Conf. Comput. Learn. Theory, 1995, pp. 23-37.
33. P. Dollár, Z. Tu, P. Perona, and S. Belongie, "Integral channel features," in Proc. British Machine Vision Conference, 2009, pp. 91.1-91.11.
34. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2001, pp. I-511-I-518.
35. S. Rosset, J. Zhu, and T. Hastie, "Boosting as a regularized path to a maximum margin classifier," J. Mach. Learn. Res., vol. 5, pp. 941-973, 2004.
36. A. Torralba, R. Fergus, and Y. Weiss, "Small codes and large databases for recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2008, pp. 1-8.
37. R. E. Schapire and Y. Singer, "Improved boosting algorithms using confidence rated predictions," Mach. Learn., vol. 37, no. 3, pp. 297-336, 1999.
38. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. New York, NY, USA: Springer, 2001.
39. A. Alahi, L. Jacques, Y. Boursier, and P. Vandergheynst, "Sparsity driven people localization with a heterogeneous network of cameras," J. Math. Imag. Vis., vol. 41, pp. 39-58, 2011.
40. K. Ali, F. Fleuret, D. Hasler, and P. Fua, "A real-time deformable detector," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 2, pp. 225-239, Feb. 2012.
41. A. Vedaldi. (2005). [Online]. Available: http://www.vlfeat.org/vedaldi/code/siftpp.html
42. M. Zervos, "Multi-camera face detection and recognition applied to people tracking," Master's Thesis, EPFL, Lausanne, Switzerland, 2013.
43. T. Ahonen, A. Hadid, and M. Pietikïinen, "Face description with local binary patterns: Application to face recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037-2041, Dec. 2006.
44. Z. Cao, Q. Yin, X. Tang, and J. Sun, "Face recognition with learning-based descriptor," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 2707-2714.
45. X. Wang, C. Zhang, and Z. Zhang, "Boosted multi-task learning for face verification with applications to web image and video search," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 142-149.