Event Mining in Multimedia Streams

Events are real-world occurrences that unfold over space and time. Event mining from multimedia streams improves the access and reuse of large media collections, and it has been an active area of research with notable progress. This paper contains a survey on the problems and solutions in event mining, approached from three aspects: event description, event-modeling components, and current event mining systems. We present a general characterization of multimedia events, motivated by the maxim of five ldquoWrdquos and one ldquoHrdquo for reporting real-world events in journalism: when, where, who, what, why, and how. We discuss the causes for semantic variability in real-world descriptions, including multilevel event semantics, implicit semantics facets, and the influence of context. We discuss five main aspects of an event detection system. These aspects are: the variants of tasks and event definitions that constrain system design, the media capture setup that collectively define the available data and necessary domain assumptions, the feature extraction step that converts the captured data into perceptually significant numeric or symbolic forms, statistical models that map the feature representations to richer semantic descriptions, and applications that use event metadata to help in different information-seeking tasks. We review current event-mining systems in detail, grouping them by the problem formulations and approaches. The review includes detection of events and actions in one or more continuous sequences, events in edited video streams, unsupervised event discovery, events in a collection of media objects, and a discussion on ongoing benchmark activities. These problems span a wide range of multimedia domains such as surveillance, meetings, broadcast news, sports, documentary, and films, as well as personal and online media collections. We conclude this survey with a brief outlook on open research directions.