Article

Rapid online learning of objects in a biologically motivated recognition architecture

Authors:

Stephan Kirstein,

Heiko Wersing,

Edgar KörnerAuthors Info & Claims

PR'05: Proceedings of the 27th DAGM conference on Pattern Recognition

Pages 301 - 308

https://doi.org/10.1007/11550518_38

Published: 31 August 2005 Publication History

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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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Hajizada EBerggold PIacono MGlover ASandamirskaya Y(2022)Interactive continual learning for robots: a neuromorphic approachProceedings of the International Conference on Neuromorphic Systems 202210.1145/3546790.3546791(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3546790.3546791
Hasler SKreger JBauer-Wersing U(2018)Interactive Incremental Online Learning of Objects Onboard of a Cooperative Autonomous Mobile RobotNeural Information Processing10.1007/978-3-030-04239-4_25(279-290)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.1007/978-3-030-04239-4_25
Wersing HKirstein SGötting MBrandl HDunn MMikhailova IGoerick CSteil JRitter HKörner E(2006)A biologically motivated system for unconstrained online learning of visual objectsProceedings of the 16th international conference on Artificial Neural Networks - Volume Part II10.1007/11840930_53(508-517)Online publication date: 10-Sep-2006
https://dl.acm.org/doi/10.1007/11840930_53

Rapid online learning of objects in a biologically motivated recognition architecture
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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PR'05: Proceedings of the 27th DAGM conference on Pattern Recognition

August 2005

510 pages

ISBN:3540287035

Editors:
Walter G. Kropatsch
PRIP, Vienna University of Technology, Austria
,
Robert Sablatnig
PRIP, Vienna University of Technology, Vienna, Austria
,
Allan Hanbury
Pattern Recognition and Image Processing Group, Institute of Computer-Aided Automation, Vienna University of Technology, Favoritenstraße 9/1832, Vienna, Austria

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Springer-Verlag

Berlin, Heidelberg

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Published: 31 August 2005

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Hajizada EBerggold PIacono MGlover ASandamirskaya Y(2022)Interactive continual learning for robots: a neuromorphic approachProceedings of the International Conference on Neuromorphic Systems 202210.1145/3546790.3546791(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3546790.3546791
Hasler SKreger JBauer-Wersing U(2018)Interactive Incremental Online Learning of Objects Onboard of a Cooperative Autonomous Mobile RobotNeural Information Processing10.1007/978-3-030-04239-4_25(279-290)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.1007/978-3-030-04239-4_25
Wersing HKirstein SGötting MBrandl HDunn MMikhailova IGoerick CSteil JRitter HKörner E(2006)A biologically motivated system for unconstrained online learning of visual objectsProceedings of the 16th international conference on Artificial Neural Networks - Volume Part II10.1007/11840930_53(508-517)Online publication date: 10-Sep-2006
https://dl.acm.org/doi/10.1007/11840930_53

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A biologically motivated visual memory architecture for online learning of objects

A biologically motivated system for unconstrained online learning of visual objects

A biologically plausible winner-takes-all architecture

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