Incorporating Geometry Information with Weak Classifiers for Improved Generic Visual Categorization

  • Gabriela Csurka
  • Jutta Willamowski
  • Christopher R. Dance
  • Florent Perronnin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


In this paper, we improve the performance of a generic visual categorizer based on the ”bag of keypatches” approach using geometric information. More precisely, we consider a large number of simple geometrical relationships between interest points based on the scale, orientation or closeness. Each relationship leads to a weak classifier. The boosting approach is used to select from this multitude of classifiers (several millions in our case) and to combine them effectively with the original classifier. Results are shown on a new challenging 10 class dataset.


Interest Point Image Patch Circular Patch Interest Point Detector Soft Margin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Gabriela Csurka
    • 1
  • Jutta Willamowski
    • 1
  • Christopher R. Dance
    • 1
  • Florent Perronnin
    • 1
  1. 1.Xerox Research Centre EuropeMeylanFrance

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