Name
Abstract | ||
|---|---|---|
This paper proposes a method for computing fast approximations to sup- port 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 syn- thesized 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 scan- ning large images, this decreases the computational complexity by a sig- nificant amount. Experimental results show that in face detection, rank deficient approximations are 4 to 6 times faster than unconstrained re- duced set systems. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | support vector,face detection,computational complexity |
Field | DocType | Citations |
Object detection,Mathematical optimization,Computer science,Support vector machine,Separable space,Artificial intelligence,Face detection,Machine learning,Computational complexity theory | Conference | 52 |
PageRank | References | Authors |
2.66 | 6 | 4 |
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 |