A self-learning system for object categorization

Abstract

We propose a learning system for object categorization which utilizes information from multiple sensors. The system learns not only prior to its deployment in a supervised mode but also in a self-learning mode. A competition based neural network learning algorithm is used to distinguish between representations of different categories. We illustrate the system application on an example of image categorization. A radar guides a selection of candidate images provided by the camera for subsequent analysis by our learning method. Radar information gets coupled with navigational information for improved localization of objects during self-learning. © 2009 Springer Berlin Heidelberg.