Abstract
We present an approach for the supervised online learning of object representations based on a biologically motivated architecture of visual processing. We use the output of a recently developed topographical feature hierarchy to provide a view-based representation of three-dimensional objects using a dynamical vector quantization approach. For a simple short-term object memory model we demonstrate real-time online learning of 50 complex-shaped objects within three hours. Additionally we propose some modifications of learning vector quantization algorithms that are especially adapted to the task of online learning and capable of effectively reducing the representational effort in a transfer from short-term to long-term memory.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Arsenio, A.: Developmental learning on a humanoid robot. In: Proc. Int. Joint Conf. Neur. Netw., Budapest, pp. 3167–3172 (2004)
Bekel, H., Bax, I., Heidemann, G., Ritter, H.: Adaptive Computer Vision: Online Learning for Object Recognition. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 447–454. Springer, Heidelberg (2004)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., et al. (eds.) Adv. Neur. Inf. Proc. Systems., vol. 7, pp. 625–632. MIT Press, Cambridge (1995)
Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)
Guedalia, I.D., London, M., Werman, M.: An on-line agglomerative clustering method for non-stationary data. Neural Computation 11(2), 521–540 (1999)
Kalinke, T., von Seelen, W.: Entropie als Mass des lokalen Informationsgehalts in Bildern zur Realisierung einer Aufmerksamkeitssteuerung. Mustererkennung, Jähne et al., pp. 627–634 (1996)
Kohonen, T.: Self-Organizing and Associative Memory, 3rd edn. Springer Series in Information Sciences. Springer, Heidelberg (1989)
Steels, L., Kaplan, F.: AIBO’s first words: The social learning of language and meaning. Evolution of Communication 4(1), 3–32 (2001)
Wersing, H., Körner, E.: Learning Optimized Features for Hierarchical Models of Invariant Object Recognition. Neural Computation 15(7), 1559–1588 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kirstein, S., Wersing, H., Körner, E. (2005). Rapid Online Learning of Objects in a Biologically Motivated Recognition Architecture. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_38
Download citation
DOI: https://doi.org/10.1007/11550518_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
Online ISBN: 978-3-540-31942-9
eBook Packages: Computer ScienceComputer Science (R0)