Learning Logical Definitions from Relations
Machine Learning
A Framework for Temporal Content Modeling of Video Data Using an Ontological Infrastructure
SKG '06 Proceedings of the Second International Conference on Semantics, Knowledge, and Grid
Dynamic pictorial ontologies for video digital libraries annotation
Workshop on multimedia information retrieval on The many faces of multimedia semantics
Building a comprehensive ontology to refine video concept detection
Proceedings of the international workshop on Workshop on multimedia information retrieval
Video Semantic Content Analysis based on Ontology
IMVIP '07 Proceedings of the International Machine Vision and Image Processing Conference
Improving the robustness of particle filter-based visual trackers using online parameter adaptation
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
An ontology infrastructure for multimedia reasoning
VLBV'05 Proceedings of the 9th international conference on Visual Content Processing and Representation
Multimedia event-based video indexing using time intervals
IEEE Transactions on Multimedia
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
Association and Temporal Rule Mining for Post-Filtering of Semantic Concept Detection in Video
IEEE Transactions on Multimedia
Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework
IEEE Transactions on Multimedia
Knowledge-assisted semantic video object detection
IEEE Transactions on Circuits and Systems for Video Technology
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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.