Recognizing 3-D objects with linear support vector machines
In this paper we propose a method for 3-D object recognition based on linear Support Vector Machines (SVMs). 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. The proposed method does not require feature extraction and performs recognition on images regarded as points of a space of high dimension. We illustrate the potential of the recognition system on a database of 7200 images of 100 different objects. The remarkable recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition, even in the presence of small amount of occlusions.
KeywordsSupport Vector Machine Object Space Linear Support Vector Machine Object Pair Margin Vector
Unable to display preview. Download preview PDF.
- 1.Bazaraa, M., Shetty, C.M.: Nonlinear programming. (John Wiley, New York, 1979).Google Scholar
- 2.Brunelli, R., Poggio, T.: Face Recognition: Features versus Templates. IEEE Trans. on PAMI, 15 (1993) 1042–1052Google Scholar
- 3.Cortes C., Vapnik, V.N.: Support Vector Network. Machine learning 20 (1995) 1–25Google Scholar
- 4.Edelman, S., Bulthoff, H., Weinshall, D.: Stimulus Familiarity Determines Recognition Strategy for Novel 3-D Objects. AI Memo No. 1138, MIT, Cambridge (1989)Google Scholar
- 6.Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: an Applications to Face Detection. Proc. Int. Conf. Computer Vision and Pattern Recognition, Puerto Rico, (1997)Google Scholar
- 9.Pontil, M., Verri, A.: Support Vector Machines for 3-D Objects Recognition. IEEE Trans. on PAMI (to appear)Google Scholar
- 12.Vapnik, V.N.: The Nature of Statistical Learning Theory. (Springer-Verlag, New York, 1995).Google Scholar