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.
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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
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DOI: https://doi.org/10.1007/11550518_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
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