Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Recognition with Local Features: the Kernel Recipe

MPG-Autoren
/persons/resource/persons84298

Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83943

Graf,  ABA
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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

Wallraven, C., Caputo, B., & Graf, A. (2003). Recognition with Local Features: the Kernel Recipe. In Ninth IEEE International Conference on Computer Vision (pp. 257-264). Los Alamitos, CA, USA: IEEE Computer Society.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-DB4D-F
Zusammenfassung
Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that this type of kernel is suitable for many standard local feature techniques in computer vision. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs using the proposed kernel perform better than standard Nearest-Neighbor techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.