A biologically motivated visual memory architecture for online learning of objects.
We present a biologically motivated architecture for object recognition that is based on a hierarchical feature-detection model in combination with a memory architecture that implements short-term and long-term memory for objects. A particular focus is the functional realization of online and incremental learning for the task of appearance-based object recognition of many complex-shaped objects. We propose some modifications of learning vector quantization algorithms that are especially adapted to the task of incremental learning and capable of dealing with the stability-plasticity dilemma of such learning algorithms. Our technical implementation of the neural architecture is capable of online learning of 50 objects within less than three hours.
Kirstein
Stephan
S
Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach am Main, Germany. stephan.kirstein@honda-ri.de
Wersing
Heiko
H
Körner
Edgar
E
D000465:Algorithms; D004193:Discrimination Learning / Q000502:physiology*; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009042:Motivation; D016571:Neural Networks, Computer; D009862:Online Systems; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation