Abstract
We present a biologically motivated system for object recognition that is capable of online learning of several objects based on interaction with a human teacher. The training is unconstrained in the sense that arbitrary objects can be freely presented in front of a stereo camera system and labeled by speech input. The architecture unites biological principles such as appearance-based representation in topographical feature detection hierarchies and context-driven transfer between different levels of object memory. The learning is fully online and thus avoids an artificial separation of the interaction into training and test phases.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wersing, H. et al. (2006). A Biologically Motivated System for Unconstrained Online Learning of Visual Objects. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_53
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DOI: https://doi.org/10.1007/11840930_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
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