Discovering Inherent Event Taxonomies from Social Media Collections

Events are becoming very popular as a tool to organize and access large media collections. An unsolved problem however, is how to define event models. Most part of the approaches so far proposed in the literature are based on a-priori knowledge, and translate into hierarchical data structures or taxonomies a more or less intuitive definition of what a given type of event is. The association of media and event models is then a consequent process, in which one tries to learn the distinctive characteristics of media associated to a certain event or sub-event. In this paper, we attempt to reverse this paradigm, inferring from a set of media collections belonging to the same event class the underlying taxonomy in an unconstrained way. As a result we obtain a hierarchy of natural clusters, largely shared by the different collections, which capture the essence of the event itself. Although it is not possible to compare the proposed approach with state-of-the-art method based on a-priori event...