Efficient Approximations for Support Vector Machines in Object Detection

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.

In  DAGM 2004

Publisher  Springer

Details

TypeInproceedings
URLhttp://www.is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/pdf2844.pdf
Pages54-61
OrganizationMax-Planck-Gesellschaft
AddressBerlin, Germany
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