Abstract
We present an architecture for the online learning of object representations based on a visual cortex hierarchy developed earlier. We use the output of a topographical feature hierarchy to provide a view-based representation of three-dimensional objects as a form of visual short term memory. Objects are represented in an incremental vector quantization model, that selects and stores representative feature maps of object views together with the object label. New views are added to the representation based on their similarity to already stored views. The realized recognition system is a major step towards shape-based immediate high-performance online recognition capability for arbitrary complex-shaped objects.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kirstein, S., Wersing, H., Körner, E. (2005). Online Learning for Object Recognition with a Hierarchical Visual Cortex Model. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_76
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DOI: https://doi.org/10.1007/11550822_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
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