Abstract
In this paper, we propose a novel supervised CCA method for multiview dimensionality reduction and classification, which simultaneously considers the class information of within-view and between-view training samples. The proposed method is applied to face and general object image recognition. The experimental results on the AT&T and Yale-B face image databases and the COIL-20 object image database show our proposed algorithm provides better recognition results on the whole than existing multiview feature extraction methods.
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References
Hotelling, H.: Relations between Two Sets of Variates. Biometrika 28, 321–377 (1936)
Sun, Q.-S., Zeng, S.-G., Liu, Y., Heng, P.-A., Xia, D.-S.: A New Method of Feature Fusion and Its Application in Image Recognition. Pattern Recognition 38, 2437–2448 (2005)
Yuan, Y.-H., Sun, Q.-S., Ge, H.-W.: Fractional-order Embedding Canonical Correlation Analysis and Its Applications to Multi-view Dimensionality Reduction and Recognition. Pattern Recognition 47, 1411–1424 (2014)
Melzer, T., Reiter, M., Bischof, H.: Appearance Models Based on Kernel Canonical Correlation Analysis. Pattern Recognition 36, 1961–1971 (2003)
Sun, T.K., Chen, S.C.: Locality Preserving CCA with Applications to Data Visualization and Pose Estimation. Image and Vision Computing 25(5), 531–543 (2007)
Sun, Q.-S., Liu, Z.-D., Heng, P.-A., Xia, D.-S.: A Theorem on the Generalized Canonical Projective Vectors. Pattern Recognition 38, 449–452 (2005)
Kim, T.-K., Kittler, J., Cipolla, R.: Discriminative Learning and Recognition of Image Set Class Using Canonical Correlations. IEEE Trans. on PAMI 29, 1005–1018 (2007)
Peng, Y., Zhang, D., Zhang, J.: A New Canonical Correlation Analysis Algorithm with Local Discrimination. Neural Processing Letters 31, 1–15 (2010)
Sun, T.K., Chen, S.C.: Class Label versus Sample Label-based CCA. Applied Mathematics and Computation 185, 272–283 (2007)
Sun, T.K., Chen, S.C., Yang, J.Y., Shi, P.F.: A novel method of combined feature extraction for recognition. In: Proceedings of IEEE International Conference on Data Mining, pp. 1043–1048 (2008)
Sharma, A., Kumar, A., Daume III, H., Jacobs, D.W.: Generalized multiview analysis: a discriminative latent space. In: Proc. IEEE Conf. on CVPR, pp. 2160–2167 (2012)
Kan, M., Shan, S., Zhang, H., Lao, S., Chen, X.: Multi-view discriminant analysis. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 808–821. Springer, Heidelberg (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. IEEE Conf. on CVPR, pp. 886–893 (2005)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. on PAMI 24, 971–987 (2002)
Georghiades, A., Belhumeur, P., Kriegman, D.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. on PAMI 23, 643–660 (2001)
Lee, K.-C., Ho, J., Kriegman, D.: Acquiring Linear Subspaces for Face Recognition under Variable Lighting. IEEE Trans. on PAMI 27, 684–698 (2005)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-20), Technical Report CUCS-005-96, February 1996
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Yuan, Y., Lu, P., Xiao, Z., Liu, J., Wu, X. (2015). A Novel Supervised CCA Algorithm for Multiview Data Representation and Recognition. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_82
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DOI: https://doi.org/10.1007/978-3-319-25417-3_82
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