Abstract
An incremental approach to the discriminative common vector (DCV) method for image recognition is presented. Two different but equivalent ways of computing both common vectors and corresponding subspace projections have been considered in the particular context in which new training data becomes available and learned subspaces may need continuous updating. The two algorithms are based on either scatter matrix eigendecomposition or difference subspace orthonormalization as with the original DCV method. The proposed incremental methods keep the same good properties than the original one but with a dramatic decrease in computational burden when used in this kind of dynamic scenario. Extensive experimentation assessing the properties of the proposed algorithms using several publicly available image databases has been carried out.
Work partially funded by FEDER and Spanish and Valencian Governments through projects TIN2009-14205-C04-03, ACOMP/2010/287, GV/2010/086 and Consolider Ingenio 2010 CSD07-00018.
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References
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 27(1), 4–13 (2005)
Murakami, H., Kumar, B.: Efficient calculation of primary images from a set of images. IEEE Trans. Patt. Analysis and Machine Intell 4(5), 511–515 (1982)
Chandrasekaran, S., Manjunath, B., Wang, Y., Winkler, J., Zhang, H.: An eigenspace update algorithm for image analysis. Graphical Models and Image Processing 59(5), 321–332 (1997)
Hall, P.M., Marshall, D., Martin, R.R.: Incremental eigenanalysis for classification. In: British Machine Vision Conference, pp. 286–295 (1998)
Ozawa, S., Toh, S.L., Abe, S., Pang, S., Kasabov, N.: Incremental learning of feature space and classifier for face recognition. Neural Netw. 18(5-6), 575–584 (2005)
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77(1-3), 125–141 (2008)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: A novel method for face recognition. In: Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, pp. 579–582 (2004)
Hall, P., Marshall, D., Martin, R.: Merging and splitting eigenspace models. IEEE Trans on Pattern Analysis and Machine Intelligence 22(9), 1042–1049 (2000)
Hall, P., Marshall, D., Martin, R.: Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image and Vision Computing 20(13-14), 1009–1016 (2002)
Gulmezoglu, M., Dzhafarov, V., Keskin, M., Barkana, A.: A novel approach to isolated word recognition. IEEE Trans. Speech and Audio Processing 7(6), 618–620 (1999)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition (2002)
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)
Nene, S., Nayar, S.K., Murase, H.: Columbia object image library (coil-20). Technical report (1996)
Tamura, A., Zhao, Q.: Rough common vector: A new approach to face recognition. In: IEEE Intl. Conf. on Syst, Man and Cybernetics, pp. 2366–2371 (2007)
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Díaz-Chito, K., Ferri, F.J., Díaz-Villanueva, W. (2010). Image Recognition through Incremental Discriminative Common Vectors. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_28
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DOI: https://doi.org/10.1007/978-3-642-17691-3_28
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