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Efficient Approximations for Support Vector Machines in Object Detection

2004

Conference Paper

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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.

Author(s): Kienzle, W. and BakIr, G. and Franz, M. and Schölkopf, B.
Book Title: DAGM 2004
Journal: Pattern Recognition, Proceedings of the 26th DAGM Symposium
Pages: 54-61
Year: 2004
Day: 0
Editors: CE Rasmussen and HH B{\"u}lthoff and B Sch{\"o}lkopf and MA Giese
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Pattern Recognition, Proceedings of the 26th DAGM Symposium
Event Place: Tübingen

Address: Berlin, Germany
Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{2844,
  title = {Efficient Approximations for Support Vector Machines in Object Detection},
  author = {Kienzle, W. and BakIr, G. and Franz, M. and Sch{\"o}lkopf, B.},
  journal = {Pattern Recognition, Proceedings of the 26th DAGM Symposium},
  booktitle = {DAGM 2004},
  pages = {54-61},
  editors = {CE Rasmussen and HH B{\"u}lthoff and  B Sch{\"o}lkopf and MA Giese},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  year = {2004}
}