Abstract
Linear discriminant analysis is a popular technique in computer vision, machine learning and data mining. It has been successfully applied to various problems, and there are numerous variations of the original approach. This paper introduces the idea of separable LDA. Towards the problem of binary classification for visual object recognition, we derive an algorithm for training separable discriminant classifiers. Our approach provides rapid training and runtime behavior and also tackles the small sample size problem. Experimental results show that the method performs robust and allows for online learning.
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References
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1990)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Fisher, R.: The Use of Multiple Measurements in Taxonomic Problems. Ann. Eugenics 7, 179–188 (1936)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. CVPR, vol. I, pp. 511–518 (2001)
Agarwal, S., Awan, A., Roth, D.: Learning to Detect Objects in Images via a Sparse, Part-based Representation. IEEE T. Pattern Anal. Machine Intell. 26, 1475–1490 (2004)
Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: Proc. CVPR, vol. II, pp. 264–272 (2003)
Garg, A., Agarwal, S., Huang, T.: Fusion of Global and Local Information for Object Detection. In: Proc. ICPR, vol. III, pp. 723–727 (2002)
Ye, J., Janardan, R., Li, Q.: Two-Dimensional Linear Discriminant Analysis. In: Saul, L., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 1569–1576. MIT Press, Cambridge (2005)
Shashua, A., Levin, A.: Linear Image Cosing for Regression and Classification using the Tensor-rank Principle. In: Proc. CVPR, vol. I, pp. 42–40 (2001)
Cover, T.: Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications to Pattern Recognition. IEEE T. on Electronic Computers 14, 326–334 (1965)
http://l2r.cs.uiuc.edu/~cogcomp/Data/Car/ (retreived Spring 2005)
Deriche, R.: Recursively Implementing the Gaussian and Its Derivatives. In: Proc. ICIP, pp. 263–267 (1992)
Leibe, B., Schiele, B.: Scale-Invariant Object Categorization using a Scale-Adaptive Mean-Shift Search. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 145–153. Springer, Heidelberg (2004)
Black, M., Jepson, A.: EigenTracking: Robust matching and tracking of articulated objects using a view-based reprenstation. Int. J. Comput. Vis. 26, 63–84 (1998)
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Bauckhage, C., Tsotsos, J.K. (2005). Separable Linear Discriminant Classification. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_40
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DOI: https://doi.org/10.1007/11550518_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
Online ISBN: 978-3-540-31942-9
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