Bootstrapping Boosted Random Ferns for discriminative and efficient object classification

Pattern Recognition

Volumn 45, Issue 9, 2012, Pages 3141-3153

  Villamizar, Michael ^a   Andrade Cetto, Juan ^a   Sanfeliu, Alberto ^a   Moreno Noguer, Francesc ^a  

^a CSIC   (Spain)

Author keywords

Boosting; Bootstrapping; Object detection; Random Ferns

Indexed keywords

BINARY CLASSIFIERS; BOOSTING; BOOTSTRAPPING; BOOTSTRAPPING SCHEME; COMPUTATIONALLY EFFICIENT; DETECTION RATES; LABELED IMAGES; LEARNING PROCESS; OBJECT CLASSIFICATION; OBJECT DETECTION; RANDOM FERNS; SAMPLE SETS; STATE-OF-THE-ART APPROACH; TRAINING IMAGE; TRAINING SETS;

PATTERN RECOGNITION; SOFTWARE ENGINEERING;

CLASSIFICATION (OF INFORMATION);

EID: 84861596906     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.03.025     Document Type: Conference Paper

References (53)

1. P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: IEEE Conference on Computer Vision and Pattern Recognition, 2001.
2. D. Lowe Distinctive image features from scale-invariant keypoints International Journal of Computer Vision 60 2 2004 91 110
3. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2005.
4. H. Bay, T. Tuytelaars, L.V. Gool, SURF: speeded up robust features, in: European Conference on Computer Vision, 2006.
5. P. Felzenszwalb, D. McAllester, D. Ramanan, A discriminatively trained, multiscale, deformable part model, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008.
6. B. Leibe, A. Leonardis, and B. Schiele Robust object detection with interleaved categorization and segmentation International Journal of Computer Vision 77 1-3 2008 259 289
7. J. Gall, V. Lempitsky, Class specific Hough forests for object detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009.
8. F. Moreno-Noguer, Deformation and illumination invariant feature point descriptor, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011.
9. F. Moreno-Noguer, A. Sanfeliu, and D. Samaras Dependent multiple cue integration for robust tracking IEEE Transactions on Pattern Analysis and Machine Intelligence 30 4 2008 670 685
10. M. Villamizar, H. Grabner, J. Andrade-Cetto, L. Gool, A. Sanfeliu, F. Moreno-Noguer, Efficient 3d object detection using multiple pose-specific classifiers, in: British Machine Vision Conference, 2011.
11. V. Lepetit, and P. Fua Keypoint recognition using randomized tree IEEE Transactions on Pattern Analysis and Machine Intelligence 28 9 2006 1465 1479
12. M. Ozuysal, P. Fua, V. Lepetit, Fast keypoint recognition in ten lines of code, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007.
13. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140
14. K.K. Sung, and T. Poggio Example-based learning for view-based human face detection IEEE Transactions on Pattern Analysis and Machine Intelligence 20 1 1998 39 51
15. C.P. Papageorgiou, M. Oren, T. Poggio, A general framework for object detection, in: International Conference on Computer Vision, 1998.
16. H. Schneiderman, T. Kanade, A statistical method for 3D object detection applied to faces and cars, in: IEEE Conference on Computer Vision and Pattern Recognition, 2000.
17. J. Shotton, A. Blake, R. Cipolla, Contour-based learning for object detection, in: International Conference on Computer Vision, 2005.
18. J. Luo, M. Boutell, R. Gray, and C. Brown Image transform bootstrapping and its applications to semantic scene classification IEEE Transactions on Systems, Man, and Cybernetics - Part B 35 3 2005 563 570
19. A. Fred, and A. Jain Combining multiple clusterings using evidence accumulation IEEE Transactions on Pattern Analysis and Machine Intelligence 27 6 2005 835 850
20. S. Munder, and D. Gavrila An experimental study on pedestrian classification IEEE Transactions on Pattern Analysis and Machine Intelligence 28 11 2006 1863 1868
21. Z. Kalal, J. Matas, K. Mikolajczyk, Weighted sampling for large scale boosting, in: British Machine Vision Conference, 2008.
22. Z. Kalal, J. Matas, K. Mikolajczyk, P-N learning: bootstrapping binary classifiers by structural constraints, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010.
23. A. Monroy, A. Eigenstetter, B. Ommer, Beyond straight lines - object detection using curvature, in: International Conference on Image Processing, 2011.
24. H. Grabner, H. Bischof, On-line boosting and vision, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006.
25. S. Hinterstoisser, V. Lepetit, S. Ilic, P. Fua, N. Navab, Dominant orientation templates for real-time detection of texture-less objects, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010.
26. M. Villamizar, F. Moreno-Noguer, J. Andrade-Cetto, A. Sanfeliu, Efficient rotation invariant object detection using boosted random Ferns, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010.
27. P. Yarlagadda, A. Monroy, B. Ommer, Voting by grouping dependent parts, in: European Conference on Computer Vision, 2010.
28. H. Riemenschneider, M. Donoser, H. Bischof, Using partial edge contour matches for efficient object category localization, in: European Conference on Computer Vision, 2010.
29. S. Maji, J. Malik, Object detection using a max-margin Hough transform, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009.
30. S. Agarwal, D. Roth, Learning a sparse representation for object detection, in: European Conference on Computer Vision, 2002.
31. K. Mikolajczyk, B. Leibe, B. Schiele, Multiple object class detection with a generative model, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006.
32. Y. Freund, and R. Schapire A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55 1 1997 119 139
33. R. Schapire Strength of weak learnability Machine Learning 5 2 1990 197 227
34. A. Demiriz, K. Bennett, and J. Shawe-Taylor Linear programming boosting via column generation Machine Learning 46 2002 225 254
35. J. Friedman, T. Hastie, and R. Tibshirani Additive logistic regression: a statistical view of boosting Annals of Statistics 28 2 2000 337 407
36. A. Levin, P. Viola, Y. Freund, Unsupervised improvement of visual detectors using cotraining, in: International Conference on Computer Vision, 2003.
37. N. Duffield, C. Lund, M. Thorup, Priority sampling for estimation of arbitrary subset sums, Journal of the ACM 54.
38. G. Escudero, L. Marquez, G. Rigau, Boosting applied to word sense disambiguation, in: European Conference on Machine Learning, 2000.
39. F. Fleuret, and D. Geman Stationary features and cat detection Journal of Machine Learning Research 9 2008 2549 2578
40. V. Ferrari, F. Jurie, C. Schmid, Accurate object detection with deformable shape models learnt from images, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007.
41. V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid Groups of adjacent contour segments for object detection IEEE Transactions on Pattern Analysis and Machine Intelligence 30 1 2008 36 51
42. R. Fergus, P. Perona, A. Zisserman, Object class recognition by unsupervised scale-invariant learning, in: IEEE Conference on Computer Vision and Pattern Recognition, 2003.
43. J. Mutch, D. Lowe, Multiclass object recognition with sparse, localized features, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006.
44. Z. Kalal, J. Matas, K. Mikolajczyk, Online learning of robust object detectors during unstable tracking, in: ICCV Online Learning for Computer Vision Workshop, 2009.
45. R.E. Schapire, and Y. Singer Improved boosting algorithms using confidence-rated predictions Machine Learning 37 3 1999 297 336
46. M. Villamizar, F. Moreno-Noguer, J. Andrade-Cetto, A. Sanfeliu, Shared random Ferns for an efficient detection of multiple categories, in: International Conference on Pattern Recognition, 2010.
47. F. Porikli, Integral histogram: a fast way to extract histograms in cartesian spaces, in: IEEE Conference on Computer Vision and Pattern Recognition, 2005.
48. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, A. Zisserman, The PASCAL visual object classes challenge 2007 (VOC2007) results, in: ICCV PASCAL Visual Recognition Callenge Workshop, 2007.
49. 〈 http://mplab.ucsd.edu 〉, The MPLab GENKI Database, GENKI-SZSL Subset.
50. V. Ferrari, F. Jurie, and C. Schmid From images to shape models for object detection International Journal of Computer Vision 87 3 2010 284 303
51. A. Toshev, B. Taskar, K. Daniilidis, Object detection via boundary structure segmentation, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010.
52. M. Fritz, B. Leibe, B. Caputo, B. Schiele, Integrating representative and discriminant models for object category detection, in: International Conference on Computer Vision, 2005.
53. C. Lampert, M. Blaschko, T. Hofmann, Beyond sliding windows: object localization by efficient subwindow search, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008.