W Kienzle, G BakIr, M Franz, and B Schölkopf
This paper proposes a method for computing fast approximations to support vector decision functions in the field of 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 synthesized input space points. In contrast to the existing method that finds the reduced set via unconstrained optimization, we impose a structural constraint on the synthetic points such that the resulting approximations can be evaluated via separable filters. For applications that require scanning an entire image, this decreases the computational complexity of a scan by a significant amount. We present experimental results on a standard face detection database.
|Published in||Advances in Neural Information Processing Systems 17|
|Address||Cambridge, MA, USA|