Pattern Recognition and Image Analysis

, Volume 17, Issue 1, pp 146–152 | Cite as

Interactive online learning

  • G. Heidemann
  • H. Bekel
  • I. Bax
  • H. Ritter
Mathematical Methods in Pattern Recognition


This paper describes a system for visual object recognition based on mobile augmented reality gear. The user can train the system to the recognition of objects online using advanced methods of interaction with mobile systems: Hand gestures and speech input control “virtual menus,” which are displayed as overlays within the camera image. Here we focus on the underlying neural recognition system, which implements the key requirement of an online trainable system—fast adaptation to novel object data. The neural three-stage architecture can be adapted in two modes: In a fast training mode (FT), only the last stage is adapted, whereas complete training (CT) rebuilds the system from scratch. Using FT, online acquired views can be added at once to the classifier, the system being operational after a delay of less than a second, though still with reduced classification performance. In parallel, a new classifier is trained (CT) and loaded to the system when ready.


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  1. 1.
    H. Bekel, I. Bax, G. Heidemann, and H. Ritter, “Adaptive Computer Vision: Online Learning for Object Recognition,” in Proc. DAGM 2004, Ed. by C. E. Rasmussen (Springer, Tubingen, Germany, 2004), pp. 447–454.Google Scholar
  2. 2.
    C. Bregler and S. M. Omohundro, “Surface Learning with Applications to Lipreading,” in Advances in Neural Information Processing Systems 1993, Eds. by J. Cowan, G. Tesauro, and J. Alspector (Morgan-Kaufmann Publishers, 1994), Vol. 6, pp. 43–50.Google Scholar
  3. 3.
    C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” in Proceedings of 4th Alvey Vision Conf., 1988, pp. 147–151.Google Scholar
  4. 4.
    T. Hastie, P. Simard, and E. Sackinger, “Learning Prototype Models For Tangent Distance,” in Advances in Neural Information Processing Systems 1994, Eds. by G. Tesauro, D. Touretzky, and T. Leen (MIT Press, 1995), Vol. 7, pp. 999–1006.Google Scholar
  5. 5.
    G. Heidernann, D. Lücke, and H. Ritter, “A System for Various Visual Classification Tasks Based on Neural Networks,” in Proc. 15th Int’l Conf. on Pattern Recognition ICPR, 2000, Barselona, Ed. by A. Sanfeliu (IEEE-CS, 2000), Vol. 1, pp. 9–12.Google Scholar
  6. 6.
    G. Heidernann and H. Ritter, “Efficient Vector Quantization Using the WTA-rule with Activity Equalization,” Neural Processing Letters 13(1), 17–30 (2001).CrossRefGoogle Scholar
  7. 7.
    G. Heidernann and H. Ritter, “Making Robots Learn to See,” in Perspectives on Adaptivity and Learning, Ed. by R. Kühn (Springer, 2003), pp. 285–309.Google Scholar
  8. 8.
    G. E. Hinton, P. Dayan, and M. Revow, “Modelling the Manifolds of Images of Handwritten Digits,” IEEE Trans. on Neural Networks 8(1), 65–74 (1997).CrossRefGoogle Scholar
  9. 9.
    I. Jolliffe, Principal Component Analysis (Springer Verlag, New York, 1986).Google Scholar
  10. 10.
    T. Kalinke and W. von Seelen, “Entropie als Mafi des lokalen Informationsgehalts in Bildern zur Realisierung einer Aufmerksamkeitssteuerung,” in Mustererkennung, 1996, Eds. by B. Jahne, P. Geifiler, H. Haufiecker, and F. Hering (Springer Verlag, Heidelberg, 1996), pp. 627–634.Google Scholar
  11. 11.
    N. Kambhatla and T. K. Leen, “Fast Non-Linear Dimension Reduction,” in Advances in Neural Information Processing Systems, 1993, Eds. by J. Cowan, G. Tesauro, and J. Alspector (Morgan Kaufmann Publishers, 1994), Vol. 6, pp. 152–159.Google Scholar
  12. 12.
    N. Kambhatla and T. K. Leen, “Dimension Reduction by Local Principal Component Analysis,” Neural Computation 9(7), 1493–1516 (1997).CrossRefGoogle Scholar
  13. 13.
    T. Kohonen, Self-Organizing Maps (Springer Verlag, 1995).Google Scholar
  14. 14.
    B. Moghaddam and A. Pentland, “Probabilistic Visual Learning for Object Representation,” IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997).CrossRefGoogle Scholar
  15. 15.
    H. Murase and S. K. Nayar, “Visual Learning and Recognition of 3-D Objects from Appearance,” Int’l J. of Computer Vision 14, 5–24 (1995).CrossRefGoogle Scholar
  16. 16.
    R. Rae, M. Fislage, and H. Ritter, “Visuelle Aufmerksamkeitssteuerung zur Unterstiitzung gestikbasierter Mensch-Maschine Interaktion,” KI—Künstliche Intelligenz, Themenheft Aktive Sehsysteme (1), 18–24 (1999).Google Scholar
  17. 17.
    D. Reisfeld, H. Wolfson, and Y. Yeshurun, “Context-Free Attentional Operators: The Generalized Symmetry Transform,” Int’l J. of Computer Vision 14, 119–130 (1995).CrossRefGoogle Scholar
  18. 18.
    H. J. Ritter, T. M. Martinetz, and K. J. Schulten, Neuronale Netze (Acldison-Wesley, München, 1992).MATHGoogle Scholar
  19. 19.
    T. D. Sanger, “Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network,” Neural Networks 2, 459–473 (1989).CrossRefGoogle Scholar
  20. 20.
    M. E. Tipping and C. M. Bishop, “Mixtures of Probabilistic Principal Component Analyzers,” Neural Computation 11(2), 443–482 (1999).CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2007

Authors and Affiliations

  • G. Heidemann
    • 1
  • H. Bekel
    • 1
  • I. Bax
    • 1
  • H. Ritter
    • 1
  1. 1.Neuroinformatics GroupBielefeld UniversityBielefeldGermany

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