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1-Click Learning of Object Models for Recognition

  • Hartmut S. Loos
  • Christoph von der Malsburg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)

Abstract

We present a method which continuously learns representations of arbitrary objects. These object representations can be stored with minimal user interaction (1-Click Learning). Appropriate training material has the form of image sequences containing the object of interest moving against a cluttered static background. Using basically the method of unsupervised growing neural gas modified to adapt to nonstationary distributions on binarized difference images, a model of the moving object is learned in real-time. Using the learned object representation the system can recognize the object or objects of the same class in single still images of different scenes. The new samples can be added to the learned object model to further improve it.

Keywords

Object Model Object Representation Minor Occlusion User Assistance Minimal User Interaction 
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 2002

Authors and Affiliations

  • Hartmut S. Loos
    • 1
  • Christoph von der Malsburg
    • 1
    • 2
  1. 1.Institut für NeuroinformatikRuhr-Universität BochumBochumGermany
  2. 2.Computer Science Dept.University of Southern CaliforniaLos AngelesUSA

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