@inproceedings{2844,
  title = {Efficient Approximations for Support Vector Machines in Object Detection},
  journal = {Pattern Recognition, Proceedings of the 26th DAGM Symposium},
  booktitle = {DAGM 2004},
  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.},
  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},
  slug = {2844},
  author = {Kienzle, W. and BakIr, G. and Franz, M. and Sch{\"o}lkopf, B.}
}