Abstract
This paper proposes a new method of feature extraction called two-dimensional optimal transform (2D-OPT) useful for appearance based object recognition. The 2D-OPT method provides a better discrimination power between classes by maximizing the distance between class centers. We first argue that the proposed 2D-OPT method works in the row direction of images and subsequently we propose an alternate 2D-OPT which works in the column direction of images. To straighten out the problem of massive memory requirements of the 2D-OPT method and as well the alternate 2D-OPT method, we introduce bi-projection 2D-OPT. The introduced bi-projection 2D-OPT method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the standard PCA/2D-PCA/Generalized 2D-PCA method, and the same has been revealed through extensive experimentations conducted on COIL-20 dataset and AT&T face dataset.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Belhumeur, P.N., Hespanha, J.P., Kreigman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Chang, K., Bowyer, K.W., Sarkar, S., Victor, B.: Comparison and combination of ear and face images for appearance-based biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1160–1165 (2003)
Hornegger, J., Niemann, H., Risack, R.: Appearance-based object recognition using optimal feature transforms. Pattern Recognition 33(2), 209–224 (2000)
Kong, H., Wang, L., Teoh, E.K., Li, X., Wang, J.-G., Venkateswaralu, R.: Generalized 2D principal component analysis for face image representation and recognition. Neural Networks 18(5-6), 585–594 (2005)
Leonardis, A., Bischof, H., Jasna, M.: Multiple Eigenspaces. Pattern Recognition 35(11), 2613–2627 (2002)
Li, M., Yuan, B.: 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters 26(5), 527–532 (2005)
Murase, H., Nayar, S.K.: Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision 14(1), 5–24 (1995)
Murase, H., Nayar, S.K.: Detection of 3D objects in cluttered scenes using hierarchical eigenspace. Pattern Recognition Letters 18(4), 375–384 (1997)
Ohba, K., Ikeuchi, K.: Detectability, Uniqueness, and Reliability of eigen windows for stable verification of partially occluded objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(9), 1043–1048 (1997)
Pentland, A., Moghaddam, B., Starner, T.: View-based and Modular Eigenspaces for Face Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 84–91 (1994)
Ribaric, S., Fratric, I.: A biometric identification system based on eigenpalm and eigenfingerprint features. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11), 1698–1709 (2005)
Schukat-Talamazzini, E.G.: Automatische Spracherkennung. Vieweg, Wiesbaden (1995)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Xiong, H., Swamy, M.N.S., Ahmad, M.O., Two-dimensional, F.L.D.: for face recognition. Pattern Recognition 38(7), 1121–1124 (2005)
Yang, J., Zhang, D., Frangi, A.F., Yang, J., Two-Dimensional, P.C.A.: A new approach to appearance based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)
Yang, J., Zhang, D., Yang, X., Yang, J.: Two-dimensional discriminant transform for face recognition. Pattern Recognition 38(7), 1125–1129 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shekar, B.H., Guru, D.S., Nagabhushan, P. (2006). Two-Dimensional Optimal Transform for Appearance Based Object Recognition. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_58
Download citation
DOI: https://doi.org/10.1007/11949619_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
eBook Packages: Computer ScienceComputer Science (R0)