Unifying multiple knowledge domains using the ARTMAP information fusion system

Author(s): Carpenter, G.A. | Ravindran, A. |

Year: 2008

Citation: Proceedings of the 11th International Conference on Information Fusion at Cologne, Germany, June 30-July 3

Abstract: Sensors working at different times, locations, and scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels that are reconciled by their implicit underlying relationships. Even when such relationships are unknown to the user, an ARTMAP information fusion system discovers a hierarchical knowledge structure for a labeled dataset. The present paper addresses the problem of integrating two or more independent knowledge hierarchies based on the same low-level classes. The new system fuses independent domains into a unified knowledge structure, discovering cross-domain rules in this process. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, ARTMAP information fusion system features distributed code representations that exploit the neural networks capacity for one-to-many learning. The fusion system software and testbed datasets are available on the Tech Lab website.

Topics: Image Analysis, Applications: Information Fusion, Models: ARTMAP,

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Cross References

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