Efficient Approximations for Support Vector Machines in Object Detection

Kienzle, W; BakIr, G; Franz, M; Schölkopf, B

doi:10.1007/978-3-540-28649-3_7

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Efficient Approximations for Support Vector Machines in Object Detection

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Kienzle, W
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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BakIr, G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Franz, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kienzle, W., BakIr, G., Franz, M., & Schölkopf, B. (2004). Efficient Approximations for Support Vector Machines in Object Detection. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 54-61). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F39F-A

Abstract

We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are
building on an existing algorithm where the set of support vectors
is replaced by a smaller so-called reduced set of synthetic
points. Instead of finding the reduced set via unconstrained
optimization, we impose a structural constraint on the synthetic
vectors such that the resulting approximation can be evaluated via
separable filters. Applications that require scanning an entire
image can benefit from this representation: when using separable
filters, the average computational complexity for evaluating a
reduced set vector on a test patch of size (h x w) drops from
O(hw) to O(h+w). We show experimental results on
handwritten digits and face detection.