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Growing Neural Gas for Vision Tasks with Time Restrictions

  • José García
  • Francisco Flórez-Revuelta
  • Juan Manuel García
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

Self-organizing neural networks try to preserve the topology of an input space by means of their competitive learning. This capacity is being used for the representation of objects and their motion. In addition, these applications usually have real-time constraints. In this work, diverse variants of a self-organizing network, the Growing Neural Gas, that allow an acceleration of the learning process are considered. However, this increase of speed causes that, in some cases, topology preservation is lost and, therefore, the quality of the representation. So, we have made a study to quantify topology preservation using different measures to establish the most suitable learning parameters, depending on the size of the network and on the available time for its adaptation.

Keywords

Topographic Function Input Space Delaunay Triangulation Gesture Recognition Vision Task 
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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References

  1. 1.
    Flórez, F., García, J.M., García, J., Hernández, A.: Representation of 2D Objects with a Topology Preserving Network. In: Proceedings of the 2nd International Workshop on Pattern Recognition in Information Systems (PRIS 2002), Alicante, pp. 267–276. ICEIS Press (2001)Google Scholar
  2. 2.
    Flórez, F., García, J.M., García, J., Hernández, A.: Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network. In: Proc. of the 5th IEEE Intern. Conference on Automatic Face and Gesture Recognition, Washington, D.C, pp. 318–323. IEEE, Inc., Los Alamitos (2001)Google Scholar
  3. 3.
    Fritzke, B.: A Growing Neural Gas Network Learns Topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  4. 4.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)Google Scholar
  5. 5.
    Martinetz, T., Schulten, K.: Topology Representing Networks. Neural Networks 7(3), 507–522 (1994)CrossRefGoogle Scholar
  6. 6.
    Cheng, G., Zell, A.: Double Growing Neural Gas for Disease Diagnosis. In: Proceedings of Artificial Neural Networks in Medicine and Biology Conference (ANNIMAB-1), Goteborg, vol. 5, pp. 309–314. Springer, Heidelberg (2000)Google Scholar
  7. 7.
    Bauer, H.-U., Pawelzik, K.R.: Quantifying the Neighborhood Preservation of Self- Organizing Feature Maps. IEEE Transactions on Neural Networks 3(4), 570–578 (1992)CrossRefGoogle Scholar
  8. 8.
    Revuelta, F.F., Chamizo, J.-M.G., Rodríguez, J.G., Sáez, A.H.: Geodesic Topographic Product: An Improvement to Measure Topology Preservation of Self-Organizing Neural Networks. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 841–850. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Villmann, T., Der, R., Herrmann, M., Martinetz, T.M.: Topology Preservation in Self- Organizing Feature Maps: Exact Definition and Measurement. IEEE Transactions on Neural Networks 8(2), 256–266 (1997)CrossRefGoogle Scholar
  10. 10.
    Kaski, S., Lagus, K.: Comparing Self-Organizing Maps. In: Vorbrüggen, J.C., von Seelen, W., Sendhoff, B. (eds.) ICANN 1996. LNCS, vol. 1112, pp. 809–814. Springer, Heidelberg (1996)Google Scholar
  11. 11.
    Martinetz, T., Schulten, K.: A “Neural-Gas” Network Learns Topologies. In: Kohonen, T., Mäkisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, vol. 1, pp. 397–402 (1991)Google Scholar
  12. 12.
    Marsland, S., Shapiro, J., Nehmzow, U.: A self-organising network that grows when required. Neural Networks 15, 1041–1058 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • José García
    • 1
  • Francisco Flórez-Revuelta
    • 1
  • Juan Manuel García
    • 1
  1. 1.Department of Computer Tecnology and ComputationUniversity of Alicante. Apdo. 99AlicanteSpain

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