Event classification in personal image collections

In this paper, we investigate event classification that is specifically developed for use in consumer family photo collections. This domain is very different from news video collections that have been the focus of research in the area of scene content classification. We determine a set of broad event classes that are relevant to personal collections. We investigate the use of a variety of high-level visual and temporal features, and determine a set of features that show good correlation with the event class. We propose a Bayesian belief network for event classification that computes the a posteriori probability of the event class given the input features. The Bayes net is trained on a large set of manually annotated consumer collections. We obtain a classification accuracy of over 70% in this challenging domain.