Abstract
Image set classification has recently attracted increasing research interest in the field of visual information processing. Different from previous methods that usually characterize set data distribution explicitly using some parametric or non-parametric models, this paper proposes a simple yet effective Partial Least Squares (PLS) regression based method, which seeks to directly learn the underlying statistical relationship between the distributions of set data and their class memberships. With no assumption on the form of set data distribution, the learned model finally reduces to an efficient linear regression from the data space to the class label space, facilitating robust classification of novel test data. Experiments on face recognition and object categorization have shown that the proposed method is competitive to the state-of-the-arts and also quite robust to the noisy set data in practical applications.
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References
Arandjelović, O., Shakhnarovich, G., Fisher, J., Cipolla, R., Darrell, T.: Face Recognition with Image Sets Using Manifold Density Divergence. In: CVPR, pp. 581–588 (2005)
Cevikalp, H., Triggs, B.: Face Recognition Based on Image Sets. In: CVPR, pp. 2567–2573 (2010)
Gross, R., Shi, J.: The CMU Motion of Body (MoBo) database. Technical Report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University (2001)
Hu, Y., Mian, A.S., Owens, R.: Sparse Approximated Nearest Points for Image Set Classification. In: CVPR (2011)
Kim, T.K., Kittler, J., Cipolla, R.: Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations. PAMI 29(6), 1005–1018 (2007)
Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-Based Face Recognition Using Probabilistic Appearance Manifolds. In: CVPR, pp. 313–320 (2003)
Leibe, B., Schiele, B.: Analyzing Appearance and Contour Based Methods for Object Categorization. In: CVPR, vol. 2, pp. 409–415 (2003)
Rosipal, R., Krämer, N.C.: Overview and Recent Advances in Partial Least Squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 34–51. Springer, Heidelberg (2006)
Rosipal, R., Trejo, L.J.: Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space. J. Machine Learning Research 2(2), 97–123 (2001)
Shakhnarovich, G., Fisher III, J.W., Darrell, T.: Face Recognition from Long-term Observations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 851–865. Springer, Heidelberg (2002)
Viola, P., Jones, M.: Robust Real-Time Face Detection. Int’l J. Computer Vision 57(2), 137–154 (2004)
Wang, R., Chen, X.: Manifold Discriminant Analysis. In: CVPR, pp. 429–436 (2009)
Wang, R., Guo, H., Davis, L., Dai, Q.: Covariance Discriminative Learning: A Natural and Efficient Approach to Image Set Classification. In: CVPR, pp. 2496–2503 (2012)
Wang, R., Shan, S., Chen, X., Dai, Q., Gao, W.: Manifold-Manifold Distance and Its Application to Face Recognition with Image Sets. IEEE Transactions on Image Processing 21(10), 4466–4479 (2012)
Yamaguchi, O., Fukui, K., Maeda, K.: Face Recognition Using Temporal Image Sequence. In: FG, pp. 318–323 (1998)
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Jin, H., Wang, R. (2013). Robust Image Set Classification Using Partial Least Squares. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_26
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DOI: https://doi.org/10.1007/978-3-642-42057-3_26
Publisher Name: Springer, Berlin, Heidelberg
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