Name
Abstract | ||
|---|---|---|
We present a new approximation scheme for support vector decision functions in object detection. In the present approach we axe 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(h(.)w) to O(h+w). We show experimental results on handwritten digits and face detection. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28649-3_7 | PATTERN RECOGNITION |
Keywords | Field | DocType |
support vector machine,face detection,computational complexity,support vector | Object detection,Decision support system,Support vector machine,Decision function,Separable space,Algorithm,Face detection,Mathematics,Statistical analysis,Computational complexity theory | Conference |
Volume | ISSN | Citations |
3175 | 0302-9743 | 4 |
PageRank | References | Authors |
0.44 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wolf Kienzle | 1 | 391 | 20.73 |
| Gökhan H. Bakir | 2 | 228 | 14.66 |
| Matthias O. Franz | 3 | 630 | 54.80 |
| Bernhard Schölkopf | 4 | 23120 | 3091.82 |
| rasmussen | 5 | 25 | 2.41 |
| Heinrich H. Bülthoff | 6 | 2524 | 384.40 |
| Martin A. Giese | 7 | 498 | 57.43 |