Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Bericht

Multi-class SVMs for Image Classification using Feature Tracking

MPG-Autoren
/persons/resource/persons83943

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

/persons/resource/persons84298

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

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Graf, A., & Wallraven, C.(2002). Multi-class SVMs for Image Classification using Feature Tracking (99).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DF40-F
Zusammenfassung
In this paper a novel representation for image classification is proposed which exploits the temporal information inherent in natural visual input. Image sequences are represented by a set of salient features which are found by tracking of visual features. In the context of a multi-class classification problem this representation is compared against a representation using only raw image data. The dataset consists of image sequences generated from a processed version of the MPI face database. We consider two types of multi-class SVMs and benchmark them against nearest-neighbor classifiers. By introducing a new set of SVM kernel functions we show that the feature representation significantly outperforms the view representation.