Image analysis – Learning systems – Trainable classifiers or pattern recognizers
Reexamination Certificate
2006-04-04
2006-04-04
Ahmed, Samir (Department: 2623)
Image analysis
Learning systems
Trainable classifiers or pattern recognizers
C382S118000, C700S047000, C706S020000
Reexamination Certificate
active
07024033
ABSTRACT:
A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.
REFERENCES:
patent: 6453307 (2002-09-01), Schapire et al.
“Learning to Detect Multi-View Faces in real-Time” by Li et al. Development and Learning, 2002. The 2nd International Conference on Jun. 12-15, 2002. pp.: 172-177.
Roth, D. , M. Yang, and N. Ahuja. “A snow-based face detector”. In Proceedings of Neural Information Processing Systems, 2000.
Rowley, H.A., S. Baluja and T. Kanade, “Neural network-based face detection”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, No. 1, pp. 23-28, Jan. 1998.
Schapire, R., Y. Freund, P. Bartlett, and W. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. In Proc. 145h International Conference on Machine Learning, pp. 322-330. Morgan Kaufmann, 1997.
Schapire, R.E. and Y. Singer. Improved boosting algorithms using confidence-rated predictions. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80-91, 1998.
Schapire, R.E. and Y. Singer.BoosTexter: A boosting-bsed system for text categorization. Machine Learning, 39(2/3):135-168, May/Jun. 2000.
Schneiderman, H. and T. Kanade. “A statistical method for 3d object detection applied to faces and cars”. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000.
Sebastiani, F., A. Sperduti and N. Valdambrini. An improved boosting algorithm and its application to automated text categorization. In Arvin Agah, Jamie Callan, and Elke Rundensteiner, ets. Preceedings of CIKM-00, 9thACM International Conference on Information and Knowledge Management, pps 78-85, MccLean, US 2000. ACM Press, New York, US.
Simard, P.Y., Y.A.L. Cun, J.S. Denker, and B. Victorri. “Transformation invariance in pattern recognition-tangent distance and tangent propagation”. In G.B. Orr and K.-R. Muller, editors, Neural Networks: Tricks of the trade. Springer, 1998.
Somol, .P., P. Pudil, J. Novoviova, and P.Paclik. “Adaptive floating search methods in feauture selection”. Pattern Recognition Letters, 20:1157-1163, 1999.
Sung, K., and T. Poggio, “Example-based Learning for View-Based Human Face Detection”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, No. 1, pp. 39-51, Jan. 1998.
Tieu, K. and P. Viola. Boosting image retrieval. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, voll:pp. 228-235, 2000.
Viola, P. and M. Jones. Robust real time object detection. In IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, Jul. 13, 2001.
Wiskott, L., J. Fellous, N. Kruger, and C.V. Malsburg. “Face recognition by elastic bunch graph matching”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775-779, 1997.
Bichsel, M. and A.P. Pentland. “Human face recognition and the face image set's topology”, CVGIP: Image Understanding, 59:254-261, 1994.
Fan, W., S. Stolfo and J. Zhang. The application of AdaBoost for Distributed, Scalable and On-line Learning. Pps 362-366, in ACM 1999.
Feraud, J., O. Bernier, and M. Collobert. “A fast and accurate face detector for indexation of face images”. In Proc. Fourth IEEE Int. Conf on Automatic face and Gesture Recognition, Grenoble, 2000.
Freund, Y. and R. Schapire. “A Decision-theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and system Sciences, 55(1):119-139, Aug. 1997.
Friedman, J., T. Hastie, and R. Tibshirani. “Additive logistic regression: a statistical view of boosting”. Technical report, Department of Statistics, sequoia Hall, Stanford Univeristy, Jul. 1998.
Gong, S., S. McKenna, and J. J. Collins. An investigation into face pose distributions. In Proc. Int'l Conf. on Autom. Face and Gesture Recog., pp 265-270, 1996.
Huang, J., S. Shao, and H. Wechsler. “Face pose discrimination using support vector machines (SVM)”. In Proceedings of International Conference Pattern recognition, Brisbane, Queensland, Australia, 1998.
Iyer, R., D. Lewis, R. Schapire, y. Singer and A. Singhal. Boosting for document routing. Ninth International Conference on Information and Knowledge Management, 2000.
Jain, A. and D. zongker. Feature selection: evaluation, application, and small sample performance. IEEE Trans. on PAMI, 19(2):153-158, 1997.
Kuchinsky, A., C.Pering, M. L. Creech, D. Freeze, B. Serra and J. Gwizdka. FotoFile: A Consumer Multimedia Organization and Retrieval System. In Proc. ACM HCI'99 Conference, 1999.
Li, Y. M., S.G. Gong, and H. Liddell. “Support vector regression and classification based multi-view face detection and recognition”. In IEEE Int. Conf. of Face & Gesture Recognition, pps. 300-305, France, Mar. 2000.
Moreno, P., B. Logan and B. Raj. A boosting approach for confidence scoring. Cambridge Research Laboratory, Technical Report Series, CRL 2001/08, Jul. 2001.
Ng, J. and S. Gong. “Performing multi-view face detection and pose estimation using a composite support vector machine across the view sphere”. in Proc. IEEE International Workshop on Recognition, Anlaysis, and tracking of Faces and gestures in Real-Time Systems, pp. 14-21, Corfu, Greece, Sep. 1999.
Osuna, E. R. Freund, and F. Girosi. “Training support vector machines: An application to face detection”. In CVPR, pps 130-136, 1997.
Papageorgiou, C.P., M. Oren, and T. Poggio. “A general framework for object detection”. In Proceedings of IEEE International Conference on Computer Vision, pp. 555-562, Bombay, India, 1998.
Pentland, A., B. Moghaddam, and T. Starner, “View-Based and Modular Eigenspaces for Face Recognition,” In Proceedings of IEEE Computer Society Conference on Comouter Vision and Pattern Recognition, pp. 84-91, 1994.
Pudill, P. J. Novovicova, and J. Kittler. floating search methods in feature selection with Nonmonotonic Criterion Functions. Department of Electronic & Electrical Engineering, University of Surrey Guildford, UK, 1994.
Li ZiQing
Zhang ZhenQiu
Zhu Long
Ahmed Samir
Kim Charles
Lyon Katrina A.
Lyon & Harr LLP
Microsoft Corp.
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