Learning rules for semantic video event annotation

Automatic semantic annotation of video events has received a large attention from the scientific community in the latest years, since event recognition is an important task in many applications. Events can be defined by spatio-temporal relations and properties of objects and entities, that change over time; some events can be described by a set of patterns. In this paper we present a framework for semantic video event annotation that exploits an ontology model, referred to as Pictorially Enriched Ontology, and ontology reasoning based on rules. The proposed ontology model includes: high-level concepts, concept properties and concept relations, used to define the semantic context of the examined domain; concept instances, with their visual descriptors, enrich the video semantic annotation. The ontology is defined using the Web Ontology Language (OWL) standard. Events are recognized using patterns defined using rules, that take into account high-level concepts and concept instances. In our approach we propose an adaptation of the First Order Inductive Learner (FOIL) technique to the Semantic Web Rule Language (SWRL) standard to learn rules. We validate our approach on the TRECVID 2005 broadcast news collection, to detect events related to airplanes, such as taxiing, flying, landing and taking off. The promising experimental performance demonstrates the effectiveness of the proposed framework.