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)


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.


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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  1. 1.
    M. E. Arterberry, L. G. Craton, and A. Yonas. Infant’s sensitivity to motion-carried information for depth and object properties. In C. E. Granrud, editor, Visual perception and cognition in infancy, Carnegie-Mellon Symposia on Cognition, 1993.Google Scholar
  2. 2.
    Marian Stewart Bartlett, Javier R. Movellan, and Terrence J. Sejnowski. Representations for Face Recognition by Independent Component Analysis. to appear in: IEEE Transactions on Neural Networks, 2002.Google Scholar
  3. 3.
    Peter N. Belhumeur, Joao Hespanha, and David J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. In ECCV (1), pages 45–58, 1996.Google Scholar
  4. 4.
    Vincent Caselles, Ron Kimmel, and Guillermo Sapiro. Geodesic Active Contours. In ICCV, pages 694–699, 1995.Google Scholar
  5. 5.
    John D. Daugman. Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech,and Signal Processing, 36:1169–1179, 1988.CrossRefzbMATHGoogle Scholar
  6. 6.
    Bernd Fritzke. A growing neural gas network learns topologies. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7, pages 625–632. MIT Press, Cambridge MA, 1995.Google Scholar
  7. 7.
    Bernd Fritzke. A self-organizing network that can follow non-stationary distributions. In ICANN’97: International Conference on Artificial Neural Networks, pages 613–618. Springer, 1997.Google Scholar
  8. 8.
    P. J. Kellman, E. S. Spelke, and K. R. Short. Infant perception of object unity from translatory motion in depth and vertical translation. Child Development, 57:72–86, 1986.CrossRefGoogle Scholar
  9. 9.
    M. Lades, J. C. Vorbrüggen, J. Buhmann, J. Lange, C. von der Malsburg, R. P. Würtz, and W. Konen. Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Transactions on Computers, 42:300–311, 1993.CrossRefGoogle Scholar
  10. 10.
    Alexander C. Loui, Charles N. Judice, and Sheng Liu. An image database for benchmarking of automatic face detection and recognition algorithms. In Proceedings of the IEEE International Conference on Image Processing, Chicago, Illinois, USA, October 4-7 1998.Google Scholar
  11. 11.
    Penio S. Penev and J. J. Atick. Local feature analysis: A general statistical theory for object representation. Network: Comp. in Neural Systems, 7(3):477–500, 1996.CrossRefzbMATHGoogle Scholar
  12. 12.
    H. A. Rowley, S. Baluja, and T. Kanade. Neural Network-Based Face Detection. IEEE Transactions on PAMI, 20(1):23–38, 1998.CrossRefGoogle Scholar
  13. 13.
    E. S. Spelke, K. Breinlinger, K. Jacobson, and A. Phillips. Gestalt relations and object perception: A developmental study. Perception, 22(12):1483–1501, 1993.CrossRefGoogle Scholar
  14. 14.
    M. Weber, M. Welling, and P. Perona. Towards automatic discovery of object categories. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head Island,South Carolina, 2000.Google Scholar
  15. 15.
    Jan Wieghardt and Hartmut S. Loos. Finding Faces in Cluttered Still Images with Few Examples. In Artificial Neural Networks—ICANN 2001, volume 2130, pages 1026–1033, Vienna, Austria, 2001.CrossRefGoogle Scholar
  16. 16.
    Laurenz Wiskott, Jean-Marc Fellous, Norbert Krüger, and Christoph von der Malsburg. Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775–779, 1997.CrossRefGoogle Scholar

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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