Learning Complex Action Patterns with CRGST

Authors:
Walter F. Bischof;Terry Caelli
Affiliations:
-;-
Venue:
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Year:
2001

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Abstract

This paper deals with the problem of automatically compiling rules which describe complex actions in terms of the spatio-temporal attributes of labeled parts. Of particular interest is the exploration of a model-based approach to induction of part attributes constrained by known properties of the generation process. The resultant algorithm is based on constraint propagation over spatio-temporal decision trees which produces Horn clause descriptions which depict the spatio-temporal properties of parts and their relations which satisfy training conditions.