Probabilistic relational learning of event models from video

This paper investigates the application of an inductive logic programming system, allied with Markov Logic Networks (MLNs), to the task of learning event models from large video datasets. A learning from interpretations setting is used to learn event models efficiently, these models define the structure of a MLN. The network parameters are obtained from discriminative learning and probabilistic inference is used to query the MLN for event recognition.