Multiscale categorical object recognition using contour fragments

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 30, Issue 7, 2008, Pages 1270-1281

  Shotton, Jamie ^a   Blake, Andrew ^b   Cipolla, Roberto ^c  

^a TOSHIBA CORPORATION   (Japan)

^b MICROSOFT RESEARCH   (United Kingdom)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Computer vision; Edge and feature detection; Feature representation; Machine learning; Object recognition; Size and shape

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER NETWORKS; LEARNING SYSTEMS;

CHAMFER MATCHING; CODE BOOKS; CONTOUR FEATURES; LOCAL DESCRIPTORS; MEAN SHIFT (MS); MULTI SCALING; PSYCHOPHYSICAL STUDIES; SLIDING-WINDOW (SW); TEST SETS; VISUAL OBJECT RECOGNITION; VISUAL RECOGNITION;

OBJECT RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 45349091203     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2007.70772     Document Type: Article

References (56)

1. http://jamie.shotton.org/work/, 2008.
2. http://www.pascal-network.org/challenges/VOC/, 2008.
3. http://www.vision.caltech.edu/Image_Datasets/Caltech101/ Caltech101.html, 2008.
4. S. Agarwal and D. Roth, "Learning a Sparse Representation for Object Detection," Proc. European Conf. Computer Vision, A. Heyden, G. Sparr, and P. Johansen, eds., pp. 113-130, May 2002.
5. D.H. Ballard, "Generalizing the Hough Transform to Detect Arbitrary Shapes," Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981.
6. H.G. Barrow, J.M. Tenenbaum, R.C. Bolles, and H.C. Wolf, "Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching," Proc. Fifth Int'l Joint Conf. Artificial Intelligence, pp. 659-663, 1977.
7. S. Belongie, J. Malik, and J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, Apr. 2002.
8. A.C. Berg, T.L. Berg, and J. Malik, "Shape Matching and Object Recognition Using Low Distortion Correspondences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 26-33, 2005.
9. I. Biederman and G. Ju, "Surface vs. Edge-Based Determinants of Visual Recognition," Cognitive Psychology, vol. 20, no. 1, pp. 38-64, Jan. 1988.
10. E. Borenstein, E. Sharon, and S. Ullman, "Combining Top-Down and Bottom-Up Segmentations," Proc. IEEE Workshop Perceptual Organization in Computer Vision, vol. 4, p. 46, 2004.
11. Y. Boykov and M.-P. Jolly, "Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images," Proc. Int'l Conf. Computer Vision, vol. 1, pp. 105-112, July 2001.
12. J. Canny, "A Computational Approach to Edge Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, Nov. 1986.
13. O. Carmichael and M. Hebert, "Shape-Based Recognition of Wiry Objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp. 1537-1552, Dec. 2004.
14. D. Comaniciu and P. Meer, "Mean Shift: A Robust Approach toward Feature Space Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
15. G. Csurka, C.R. Dance, L. Fan, J. Willamowski, and C. Bray, "Visual Categorization with Bags of Keypoints," Proc. European Conf. Computer Vision Int'l Workshop Statistical Learning in Computer Vision 2004.
16. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition vol. 2, pp. 886-893, June 2005.
17. J. De Winter and J. Wagemans, "Contour-Based Object Identification and Segmentation: Stimuli, Norms and Data, and Software Tools," Behavior Research Methods, Instruments, and Computers, vol. 36, no. 4, pp. 604-624, Nov. 2004.
18. P. Dollár, Z. Tu, and S. Belongie, "Supervised Learning of Edges and Object Boundaries," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1964-1971, 2006.
19. L. Fei-Fei, R. Fergus, and P. Perona, "One-Shot Learning of Object Categories," IEEE Trans. Pattern Analysis and Machine Intelligence vol. 28, no. 4, pp. 594-611, Apr. 2006.
20. P.F. Felzenszwalb, "Learning Models for Object Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 1056-1062, Dec. 2001.
21. P.F. Felzenszwalb and D.P. Huttenlocher, "Distance Transforms of Sampled Functions," technical report, Cornell Univ., 2004.
22. P.F. Felzenszwalb and D.P. Huttenlocher, "Pictorial Structures for Object Recognition," Int'l J. Computer Vision, vol. 61, no. 1, pp. 55-79, Jan. 2005.
23. R. Fergus, P. Perona, and A. Zisserman, "Object Class Recognition by Unsupervised Scale-Invariant Learning," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 264-271, 2003.
24. R. Fergus, P. Perona, and A. Zisserman, "A Visual Category Filter for Google Images," Proc. European Conf. Computer Vision, T. Pajdla and J. Matas, eds., pp. 242-256, May 2004.
25. V. Ferrari and C. Schmid, "Groups of Adjacent Contour Segments for Object Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, 2007.
26. V. Ferrari, T. Tuytelaars, and L. Van Gool, "Object Detection by Contour Segment Networks," Proc. European Conf. Computer Vision, A. Leonardis, H. Bischof, and A. Pinz, eds., pp. 14-28, May 2006.
27. J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression: A Statistical View of Boosting," Annals of Statistics vol. 28, no. 2, pp. 337-407, 2000.
28. D.M. Gavrila, "Multi-Feature Hierarchical Template Matching Using Distance Transforms," Proc. Int'l Conf. Pattern Recognition, vol. 1, pp. 439-444, Aug. 1998.
29. D.M. Gavrila, "Pedestrian Detection from a Moving Vehicle," Proc. European Conf. Computer Vision, D. Vernon, ed., pp. 37-49, June 2000.
30. D.P. Huttenlocher and W.J. Rucklidge, "A Multi-Resolution Technique for Comparing Images Using the Hausdorff Distance," Technical Report TR 92-1321, Dept. of Computer Science, Cornell Univ., Dec. 1992.
31. M.P. Kumar, P.H.S. Torr, and A. Zisserman, "Extending Pictorial Structures for Object Recognition," Proc. British Machine Vision Conf., 2004.
32. M.P. Kumar, P.H.S. Torr, and A. Zisserman, "OBJ CUT," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 18-25, June 2005.
33. B. Leibe and B. Schiele, "Interleaved Object Categorization and Segmentation," Proc. British Machine Vision Conf., vol. 2, pp. 264-271, 2003.
34. B. Leibe and B. Schiele, "Scale Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search," Proc. 26th Pattern Recognition Symp., pp. 145-153, June 2004.
35. B. Leibe, E. Seemann, and B. Schiele, "Pedestrian Detection in Crowded Scenes," Proc. IEEE Conf. Computer Vision and Pattern Recognition vol. 1, pp. 878-885, June 2005.
36. D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004.
37. D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, 1982.
38. D.R. Martin, C.C. Fowlkes, and J. Malik, "Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 530-549, May 2004.
39. K. Mikolajczyk, A. Zisserman, and C. Schmid, "Shape Recognition with Edge-Based Features," Proc. British Machine Vision Conf., vol. 2, pp. 779-788, Sept. 2003.
40. R.C. Nelson and A. Selinger, "A Cubist Approach to Object Recognition," Proc. Int'l Conf. Computer Vision, pp. 614-621, Jan. 1998.
41. C.F. Olson and D.P. Huttenlocher, "Automatic Target Recognition by Matching Oriented Edge Pixels," IEEE Trans. Image Processing, vol. 6, no. 1, pp. 103-113, Jan. 1997.
42. A. Opelt, A. Pinz, and A. Zisserman, "A Boundary-Fragment-Model for Object Detection," Proc. European Conf. Computer Vision, A. Leonardis, H. Bischof, and A. Pinz, eds., vol. 2, pp. 575-588, May 2006.
43. A. Opelt, A. Pinz, and A. Zisserman, "Incremental Learning of Object Detectors Using a Visual Shape Alphabet," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 3-10, June 2006.
44. C. Rother, V. Kolmogorov, and A. Blake, "GrabCut - Interactive Foreground Extraction Using Iterated Graph Cuts," ACM Trans. Graphics, vol. 23, no. 3, pp. 309-314, Aug. 2004.
45. J. Shotton, "Contour and Texture for Visual Recognition of Object Categories," PhD dissertation, Univ. of Cambridge, Mar. 2007.
46. J. Shotton, A. Blake, and R. Cipolla, "Contour-Based Learning for Object Detection," Proc. Int'l Conf. Computer Vision, vol. 1, pp. 503-510, Oct. 2005.
47. J. Sivic, B.C. Russel, A.A. Efros, A. Zisserman, and W.T. Freeman, "Discovering Objects and Their Localization in Images," Proc. Int'l Conf. Computer Vision, vol. 1, pp. 370-377, Oct. 2005.
48. J. Sivic and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos," Proc. Int'l Conf. Computer Vision, vol. 2, pp. 1470-1477, Oct. 2003.
49. B. Stenger, A. Thayananthan, P.H.S. Torr, and R. Cipolla, "Model-Based Hand Tracking Using a Hierarchical Bayesian Filter," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1372-1384, Sept. 2006.
50. A. Torralba, K.P. Murphy, and W.T. Freeman, "Sharing Visual Features for Multiclass and Multiview Object Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 854-869, May 2007.
51. K. Toyama and A. Blake, "Probabilistic Tracking with Exemplars in a Metric Space," Int'l J. Computer Vision, vol. 48, no. 1, pp. 9-19, June 2002.
52. P. Viola and M.J. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, Dec. 2001.
53. J. Winn and J. Shotton, "The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 37-44, June 2006.
54. J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid, "Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study," Int'l J. Computer Vision, vol. 73, no. 2, pp. 213-238, 2007.
55. S. Zheng, Z. Tu, and A.L. Yuille, "Detecting Object Boundaries Using Low-, Mid- and High-Level Information," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
56. X. Zhu and Z. Ghahramani, "Learning from Labeled and Unlabeled Data with Label Propagation," Technical Report CMU-CALD-02-107, Carnegie Mellon Univ., 2002.