Videos of complex events are difficult to represent solely as
bags of low level features. Increasingly, supervised concepts
or attributes are being employed as the intermediate representation
of such videos. We propose a probabilistic framework
that models the conditional relationships between the
concepts and events and devise an approximate yet tractable
solution to infer the posterior distribution to perform event
classification. Using noisy outputs of pre-trained concept detectors,
we learn semantic and visual dependencies between
event and concept pairs. The co-occurrence between concept
pairs is also learned as a marginal over training samples. The
proposed method then employs the learned prior, as well as
the probabilities of occurrence of specific concepts in a test
video to infer the probability of each event using weighted average
one-dependence estimation. The evaluation shows that
our method improves event classification compared to recent
literature on the TRECVID data set