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× w drops from O(h· w) to O(h+w). We show experimental results on handwritten digits and face detection.
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Kienzle, W., Bakır, G., Franz, M., Schölkopf, B. (2004). Efficient Approximations for Support Vector Machines in Object Detection. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_7
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DOI: https://doi.org/10.1007/978-3-540-28649-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
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