Abstract
This paper proposes the kernel orthogonal mutual subspace method (KOMSM) for 3D object recognition. KOMSM is a kernel-based method for classifying sets of patterns such as video frames or multi-view images. It classifies objects based on the canonical angles between the nonlinear subspaces, which are generated from the image patterns of each object class by kernel PCA. This methodology has been introduced in the kernel mutual subspace method (KMSM). However, KOMSM is different from KMSM in that nonlinear class subspaces are orthogonalized based on the framework proposed by Fukunaga and Koontz before calculating the canonical angles. This orthogonalization provides a powerful feature extraction method for improving the performance of KMSM. The validity of KOMSM is demonstrated through experiments using face images and images from a public database.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Oja, E.: Subspace methods of pattern recognition. Research Studies Press (1983)
Fukunaga, K., Koontz, W.C.G.: Application of the Karhunen-Loeve expansion to feature selection and ordering. IEEE Trans. Computers C-19, 311–318 (1970)
Maeda, K., Watanabe, S.: A pattern matching method with local structure: Trans. IEICE J68-D(3), 345–352 (1985) (in Japanese)
Sakano, H., Mukawa, N., Nakamura, T.: Kernel Mutual Subspace Method and its Application for Object Recognition. Electronics and Communications in Japan 88, 45–53 (2005)
Wolf, L., Shashua, A.: Kernel principal angles for classification machines with applications to image sequence interpretation. In: CVPR 2003, pp. 635–642 (2003)
Fukui, K., Yamaguchi, O.: Face recognition using multi-viewpoint patterns for robot vision. In: ISRR. 11th International Symposium of Robotics Research, pp. 192–201 (2003)
Nagao, K., Sohma, M.: Weak orthogonalization of face and perturbation for recognition. In: CVPR 1998, pp. 845–852 (1998)
Kawahara, T., Nishiyama, M., Yamaguchi, O.: Face recognition by orthogonal mutual subspace method. CVIM-151 151, 17–24 (2005) (in Japanese)
Kim, T.-K., Kittler, J., Cipolla, R.: Incremental Learning of Locally Orthogonal Subspaces for Set-based Object Recognition. In: BMVC 2006, pp. 559–568 (2006)
Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear principal component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)
Fukui, K., Stenger, B., Yamaguchi, O.: A framework for 3D object recognition using the kernel constrained mutual subspace method. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 315–324. Springer, Heidelberg (2006)
Chatelin, F.: Eigenvalues of matrices. John Wiley & Sons, Chichester (1993)
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: CVPR 2003, pp. 409–415 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fukui, K., Yamaguchi, O. (2007). The Kernel Orthogonal Mutual Subspace Method and Its Application to 3D Object Recognition. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-76390-1_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76389-5
Online ISBN: 978-3-540-76390-1
eBook Packages: Computer ScienceComputer Science (R0)