Abstract
Many researchers have reported that recognition accuracy improves when several images are continuously input into a recognition system. We call this recognition scheme a continuous observation- based scheme (CObS). The CObS is not only a useful and robust object recognition technique, it also offers a new direction in statistical pattern classification research. The main problem in statistical pattern recognition for the CObS is how to define the measure of similarity between two distributions. In this paper, we introduce some classifiers for use with continuous observations. We also experimentally demonstrate the effectiveness of continuous observation by comparing various classifiers.
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References
Ken-ichi Maeda and Sadaichi Watanabe, ”Pattern Matching Method with Local Structure”, Trans. IEICE(D), Vol. 68-D. No. 3, pp. 345–352(1985) (in Japanese)
O. Yamaguchi, K. Fukui and K. Maeda, ”Face Recognition using Temporal Image Sequence”, In Proc. IEEE 4thtl. Conf. on Face and Gesture Recognition, pp. 318–323 (1998)
H. Sakano, et. al., ”Kernel mutual subspace method for robust facial image recognition”, in Proc. IEEE Intl. Conf. of Knowledge Engineering System, pp. 245–248, (2000)
H. Sakano, ”Kernel Mutual Subspace Method for Object Recognition”, Trans. IEICE(D-II), Vol. J84-D-II, No. 8, pp. 1549–1556, (2001) (in Japanese)
B. Schölkopf, et al., ”Nonlinear component analysis as a kernel eigenvalue problem”, Neural Computation, Vol. 10, pp. 1299–1319 (1998)
S. Watanabe and N. Pakvasa, ”Subspace method of pattern recognition”, Proc. 1st IJCPR, pp. 25–32 (1973)
M. Turk and A. Pentland, ”Recognition Using Eigenface”, Proc. CVPR, pp. 568–591 (1991)
H. Murase and S. K. Nayer, “Visual learning and recognition of 3-D object from appearance”, International Journal of Computer Vision, Vol. 14, pp. 5–24, (1995)
M. A. Aizerman, et. al., “Theoretical foundations of the potential function method in pattern recognition learning”, Automation and Remote Control, Vol. 25, pp. 821–837, (1964)
F. Chatelin, ”Veleurs propres de matrices”, Masson, Paris (1988)
D. B. Graham and N. S. Allinson, ”Characterizing Virtual Eigensignatures for General Purpose Face Recognition”, in H. Wechsler, et al. ed. ”Face Recognition From Theory to Applications”, Springer Verlag, (1998)
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Sakano, H., Suenaga, T. (2002). Classifiers under Continuous Observations. In: Caelli, T., Amin, A., Duin, R.P.W., de Ridder, D., Kamel, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2002. Lecture Notes in Computer Science, vol 2396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-70659-3_84
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DOI: https://doi.org/10.1007/3-540-70659-3_84
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