Abstract
Cascades of classifiers constitute an important architecture for fast object detection. While boosting of simple (weak) classifiers provides an established framework, the design of similar architectures with more powerful (strong) classifiers has become the subject of current research. In this paper, we focus on greedy strategies recently proposed in the literature that allow to learn sparse Support Vector Machines (SVMs) without the need to train full SVMs beforehand. We show (i) that asymmetric data sets that are typical for object detection scenarios can be successfully handled, and (ii) that the complementary training of two sparse SVMs leads to sequential two-stage classifiers that slightly outperform a full SVM, but only need about 10% kernel evaluations for classifying a pattern.
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References
Keerthi, S.S., Chapelle, O., DeCoste, D.: Building support vector machines with reduced classifier complexity. J. Mach. Learning Res. 7, 1493–1515 (2006)
Rätsch, G.: Benchmark data sets, http://ida.first.fraunhofer.de/projects/bench/benchmarks.htm
Bach, F.R., Heckerman, D., Horvitz, E.: Considering cost asymmetry in learning classifiers. J. Mach. Learning Res. 7, 1713–1741 (2006)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comp. Vision 57(2), 137–154 (2004)
Romdhani, S., Torr, P., Schölkopf, B., Blake, A.: Efficient face detection by a cascaded support-vector machine expansion. Proc. Royal Soc. A 460, 3283–3297 (2004)
Franc, V., Hlavać, V.: Greedy algorithm for a training set reduction in the kernel methods. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 426–433. Springer, Heidelberg (2003)
Sahbi, H., Geman, D.: A hierarchy of support vector machines for pattern detection. J. Mach. Learning Res. 7, 2087–2123 (2006)
Rätsch, M., Romdhani, S., Teschke, G., Vetter, T.: Over-complete wavelet approximation of a support vector machine for efficient classification. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) Pattern Recognition. LNCS, vol. 3663, pp. 351–360. Springer, Heidelberg (2005)
Zapién, K., Fehr, J., Burkhardt, H.: Fast support vector machine classification using linear SVMs. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 3, pp. 366–369. IEEE, Los Alamitos (2006)
Heisele, B., Serre, T., Prentice, S., Poggio, T.: Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition 36(9), 2007–2017 (2003)
Wu, M., Schölkopf, B., Bakir, G.: A direct method for building sparse kernel learning algorithms. J. Mach. Learning Res. 7, 603–624 (2006)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vision 60(2), 91–110 (2004)
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Karim, R., Bergtholdt, M., Kappes, J., Schnörr, C. (2007). Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_40
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DOI: https://doi.org/10.1007/978-3-540-74936-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74933-2
Online ISBN: 978-3-540-74936-3
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