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Support Vector Machines for 3D Object Recognition
June 1998 (vol. 20 no. 6)
pp. 637-646

Abstract—Support Vector Machines (SVMs) have been recently proposed as a new technique for pattern recognition. Intuitively, given a set of points which belong to either of two classes, a linear SVM finds the hyperplane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyperplane. The hyperplane is determined by a subset of the points of the two classes, named support vectors, and has a number of interesting theoretical properties. In this paper, we use linear SVMs for 3D object recognition. We illustrate the potential of SVMs on a database of 7,200 images of 100 different objects. The proposed system does not require feature extraction and performs recognition on images regarded as points of a space of high dimension without estimating pose. The excellent recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition.

[1] S. Akamatsu, T. Sasaki, H. Fukumachi, and Y. Suenaga, "A Robust Face Identification Scheme - KL Expansion of an Invariant Feature Space," SPIE Proc. Intelligent Robts and Computer Vision X: Algorithms and Techniques, vol. 1,607, pp. 71-84, 1991.
[2] M. Bazaraa and C.M. Shetty, Nonlinear Programming.New York, NY: John Wiley, 1979.
[3] V. Blanz, B. Scholkopf, H. Bulthoff, C. Burges, V.N. Vapnik, and T. Vetter, "Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models," Proc of ICANN'96, LNCS, vol. 1,112, pp. 251-256, 1996.
[4] R. Brunelli and T. Poggio, "Face Recognition: Features vs. Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1,042-1,053, Oct. 1993.
[5] C. Cortes and V.N. Vapnik, "Support Vector Network," Machine Learning, vol. 20, pp. 1-25, 1995.
[6] S. Edelman, H. Bulthoff, and D. Weinshall, "Stimulus Familiarity Determines Recognition Strategy for Novel 3-D Objects," AI Memo, no. 1,138, Cambridge, Mass.: Massachusetts Institute of Tech nology, 1989.
[7] D.P. Huttenlocher, G.A. Klanderman, and W.J. Rucklidge, “Comparing Images Using the Hausdorff Distance,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, Sept. 1993.
[8] H. Murase and S.K. Nayar, "Visual Learning and Recognition of 3-D Object From Appearance," Int. J. Computer Vision, vol. 14, pp. 5-24, 1995.
[9] E. Osuna, R. Freund, and F. Girosi, Training Support Vector Machines: An Application to Face Detection Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 17-19, 1997.
[10] A. Pentland, B. Moghaddam, and Starner, "View-Based and Modular Eigenspaces for Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1994, pp. 84-91.
[11] T. Poggio and S. Edelman, "A Network That Learns to Recognize Three-Dimensional Objects," Nature, vol. 343, pp. 263-266, 1990.
[12] M. Tarr and S. Pinker, "Mental Rotation and Orientation-Dependence in Shape Recognition," Cognitive Psychology, vol. 21, pp. 233-282, 1989.
[13] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, pp. 71-86, 1991.
[14] B. Scholkopf, K. Sung, C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, "Comparing Support Vector Machines With Gaussian Kernels to Radial Basis Function Classifiers," AI Memo, no. 1599; CBCL Paper, no. 142.Cambridge, Mass.: Massachusetts Institute of Tech nology, 1996.
[15] V.N. Vapnik and A.J. Chervonenkis, "On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities," Theory Probability Appl., vol. 16, pp. 264-280, 1971.
[16] V.N. Vapnik, The Nature of Statistical Learning Theory.New York, NY: Springer-Verlag, 1995.
[17] J. Weng, "Cresceptron and SHOSLIF: Toward Comprehensive Visual Learning." S.K. Nayar and T. Poggio, eds. Early Visual Learning. Oxford Univ. Press, 1996.

Index Terms:
Support vector machines, optimal separating hyperplane, appearance-based object recognition, pattern recognition.
Massimiliano Pontil, Alessandro Verri, "Support Vector Machines for 3D Object Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 637-646, June 1998, doi:10.1109/34.683777
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