A novel distribution-based feature for rapid object detection

Neurocomputing

Volumn 74, Issue 17, 2011, Pages 2767-2779

  Shen, Jifeng ^a   Sun, Changyin ^a   Yang, Wankou ^a   Wang, Zhenyu ^a   Sun, Zhongxi ^a,b  

^a SOUTHEAST UNIVERSITY   (China)

^b HOHAI UNIVERSITY   (China)

Author keywords

Adaptive projection MBLBP; Asymmetric Gentle Adaboost; LBP; Object detection

Indexed keywords

ADAPTIVE PROJECTION-MBLBP; ASYMMETRIC GENTLE ADABOOST; CASCADE STRUCTURES; CONVERGENCE RATES; DISCRIMINATIVE ABILITY; DISCRIMINATIVE FEATURES; GENTLE ADABOOST; HAAR-LIKE FEATURES; HUMAN FACES; LBP; LOCAL BINARY PATTERNS; LOCAL STRUCTURE; OBJECT CATEGORIES; OBJECT DETECTION; OBJECT DETECTORS; OPTIMAL SEPARATING HYPERPLANE; RIGID OBJECTS; RUNNING SPEED; TRAINING DATA;

ADAPTIVE BOOSTING; OBJECT RECOGNITION;

DETECTORS;

ALGORITHM; ARTICLE; CAR; CONTROLLED STUDY; DISCRIMINATIVE FEATURE; FACE; ILLUMINATION; IMAGE PROCESSING; IMAGING; LOCAL BINARY PATTERN; PRIORITY JOURNAL; RAPID OBJECT DETECTION;

EID: 80052941911     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.03.032     Document Type: Article

References (55)

1. Enzweiler M., Gavrila D.M. Monocular pedestrian detection: survey and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009, 31(12):2179-2195.
2. Kasturi R., Goldgof D., Soundararajan P., Manohar V., Garofolo J., Bowers R., Boonstra M., Korzhova V., Jing Z. Framework for performance evaluation of face, text, and vehicle detection and tracking in video: data, metrics, and protocol. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009, 31(2):319-336.
3. Viola P., Jones M. Rapid object detection using a boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition 2001, 511:511-518.
4. C.P. Papageorgiou, M. Oren, T. Poggio, A general framework for object detection, in: Sixth International Conference on Computer Vision, 1998, pp. 555-562.
5. Lienhart R., Maydt J. An extended set of Haar-like features for rapid object detection. International Conference on Image Processing 2002, 1:900-903.
6. M. Jones, P. Viola, Fast multi-view face detection, 〈〉. http://www.merl.com/papers/docs/TR2003-96.pdf.
7. S. Li, L. Zhu, Z. Zhang, A. Blake, H. Zhang, H. Shum, Statistical learning of multi-view face detection, in: Proceedings of the 7th European Conference on Computer Vision-Part IV, 2002, pp. 67-81.
8. C. Huang, H. Ai, Y. Li, S. Lao, Learning sparse features in granular space for multi-view face detection, in: International Conference on Automatic Face and Gesture Recognition, 2006, pp. 401-406.
9. Gao W., Ai H., Lao S. Adaptive contour features in oriented granular space for human detection and segmentation. IEEE Conference on Computer Vision and Pattern Recognition 2009.
10. Ojala T., Pietikäinen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(7):971-987.
11. L. Zhang, R. Chu, S. Xiang, S. Liao, S. Li, Face detection based on multi-block LBP representation, in: 2nd International Conference on Biometrics, 2007, pp. 11-18.
12. Froba B., Ernst A. Face detection with the modified census transform. IEEE International Conference on Automatic Face and Gesture Recognition 2004, 91-96.
13. Roy A., Marcel S. Haar local binary pattern feature for fast illumination invariant face detection. BMVC 2009 2009.
14. Shen C., Paisitkriangkrai S., Zhang J. Face detection from few training examples. IEEE International Conference on Image Processing 2008, 2764-2767.
15. Tuzel O., Porikli F., Meer P. Human detection via classification on Riemannian manifolds. IEEE Conference on Computer Vision and Pattern Recognition 2007, 1-8.
16. Dalal N., Triggs B. Histograms of oriented gradients for human detection. IEEE Conference on Computer Vision and Pattern Recognition 2005, 881:886-893.
17. Levi K., Weiss Y. Learning object detection from a small number of examples: the importance of good features. IEEE Conference on Computer Vision and Pattern Recognition 2004, 52:53-60.
18. Lowe D.G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2004, 60(2):91-110.
19. Jin Z., et al. Face detection using template matching and skin-color information. Neurocomputing 2007, 70(4-6):794-800.
20. Juang C.F., Sun W.K., Chen G.C. Object detection by color histogram-based fuzzy classifier with support vector learning. Neurocomputing 2009, 72(10-12):2464-2476.
21. Colmenarez A., Huang T. Face detection with information-based maximum discrimination. IEEE Conference on Computer Vision and Pattern Recognition 1997, 782.
22. Osuna E., Freund R., Girosit F. Training support vector machines: an application to face detection. IEEE Conference on Computer Vision and Pattern Recognition 1997, 130-136.
23. Juang C.F., Shiu S.-J. Using self-organizing fuzzy network with support vector learning for face detection in color images. Neurocomputing 2008, 71(16-18):3409-3420.
24. Rowley H.A., Baluja S., Kanade T. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20(1):23-38.
25. Yang M.H., Roth D., Ahuja N. A SNoW-based face detector. Advances in Neural Information Processing Systems 2000, 12:855-861.
26. Fleuret F., Geman D. Coarse-to-fine face detection. International Journal of Computer Vision 2001, 41(1):85-107.
27. Xiao R., Zhu L., Zhang H.J. Boosting chain learning for object detection. IEEE International Conference on Computer Vision 2003, 701:709-715.
28. Sochman J., Matas J. WaldBoost-learning for time constrained sequential detection. IEEE Conference on Computer Vision and Pattern Recognition 2005, 152:150-156.
29. Bourdev L., Brandt J. Robust object detection via soft cascade. IEEE Conference on Computer Vision and Pattern Recognition 2005, 232:236-243.
30. Xiao R., Zhu H., Sun H., Tang X. Dynamic cascades for face detection. IEEE International Conference on Computer Vision 2007, 1-8.
31. Pham M.T., Hoang V.D., Cham T.J. Detection with multi-exit asymmetric boosting. IEEE Conference on Computer Vision and Pattern Recognition 2008, 1-8.
32. Yan S., Shan S., Chen X., Gao W., Chen J. Matrix-structural learning (MSL) of cascaded classifier from enormous training set, computer vision and pattern recognition. IEEE International Conference on Computer Vision 2007, 1-7.
33. Shen J., Yang W., Sun C. Learning discriminative features based on distribution. International Conference on Pattern Recognition 2010, 1401-1404.
34. Zhang B., Shan S., Chen X., Gao W. Histogram of Gabor phase pattern: a novel object representation for face recognition. IEEE Transactions on Image Processing 2007, 1(1):57-68.
35. Zhao G., Pietikäinen M. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29(6):915-928.
36. T. Kim, D. Kim, MMI-based optimal LBP code selection for facial expression recognition, in: IEEE International Symposium on Signal Processing and Information Technology, 2009, pp. 384-391.
37. Heikkilä M., Pietikäinen M. A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28(4):657-662.
38. Guo Y., Zhao G., Chen J., Pietikäinen M., Xu Z. Dynamic texture synthesis using a spatial temporal descriptor. IEEE International Conference on Image Processing 2009.
39. Guo Z., Zhang L., Zhang D. Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition 2010, 43(3):706-719.
40. Hadid A., Pietikäinen M. Combining appearance and motion for face and gender recognition from videos. Pattern Recognition 2009, 42(11):2818-2827.
41. Guo Z., Zhang L., Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 2010, 19(6):1657-1663.
42. Zabih R., Woodfill J. A non-parametric approach to visual correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996.
43. Heikkil M., Pietikäinen M., Schmid C. Description of interest regions with local binary patterns. Pattern Recognition 2009, 42(3):425-436.
44. Yan S., Shan S., Chen X., Gao W. Locally assembled binary (LAB) feature with feature-centric cascade for fast and accurate face detection. IEEE Conference on Computer Vision and Pattern Recognition 2008, 1-7.
45. Pavani S.K., Delgado D., Frangi A.F. Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recognition 2009, 43(1):160-172.
46. Viola P., Jones M. Fast and robust classification using asymmetric adaboost and a detector cascade. Advances in Neural Information Processing Systems 2002, 1311-1318.
47. H. Masnadi-Shirazi and N. Vasconcelos, Asymmetric boosting, in: Proceedings of the Twenty-Fourth International Conference in Machine Learning, 2007, vol. 227, pp. 609-619.
48. Li Q., Mao Y., Wang Z., Xiang W. Cost-sensitive boosting: fitting an additive asymmetric logistic regression model. Advances in Machine Learning 2009, 5828:1611-3349.
49. Agarwal S., Awan A., Roth D. Learning to detect objects in images via a sparse, part-based representation. IEEE Transactions on Patterns Analysis and Machine Intelligence 2004, 26(11):1475-1490.
50. Everingham M., Van Gool L., Williams C.K.I., Winn J., Zisserman A. The PASCAL visual object classes (VOC) challenge. International Journal of Computer Vision 2010, 88(2):303-338.
51. Chen J., Chen X., Yang J., Shan S., Wang R., Gao W. Optimization of a training set for more robust face detection. Pattern Recognition 2009, 42(11):2828-2840.
52. Mutch J., Lowe D.G. Multiclass object recognition with sparse. Localized features. IEEE Conference on Computer Vision and Pattern Recognition 2006, 11-18.
53. B. Wu and R. Nevatia, Cluster boosted tree classifier for multi-view, multi-pose object detection, in: IEEE 11th International Conference on Computer Vision, vol. 1-8, 2007.
54. Fritz M., Leibe B., Caputo B., Schiele B. Integrating representative and discriminative models for object category detection. IEEE International Conference on Computer Vision 2005, 1363-1370.
55. Lampert C.H., Blaschko M.B., Hofmann T. Beyond sliding windows. Object localization by efficient subwindow search. IEEE Conference on Computer Vision and Pattern Recognition 2008, 1-8.