Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Konferenzbeitrag

Efficient Approximations for Support Vector Machines in Object Detection

MPG-Autoren
/persons/resource/persons84012

Kienzle,  W
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83791

BakIr,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83919

Franz,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://link.springer.com/content/pdf/10.1007%2F978-3-540-28649-3_7.pdf
(Verlagsversion)

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Kienzle, W., BakIr, G., Franz, M., & Schölkopf, B. (2004). Efficient Approximations for Support Vector Machines in Object Detection. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 54-61). Berlin, Germany: Springer.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-F39F-A
Zusammenfassung
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.