Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning

The constantly increasing popularity and ubiquity of videos urges efficient auto-mated mechanisms for processing video contents, which is a big challenge due to the huge gap between what software agents can obtain from signal processing and what humans can comprehend based on cognition, knowledge, and experience. Automatically extracted low-level video features typically do not correspond to concepts, persons, and events depicted in videos. To narrow the Semantic Gap, the depicted concepts and their spatial relations can be described in a machine-interpretable form using formal definitions from structured data resources. Rule-based mechanisms are efficient in describing the temporal information of actions and video events. The fusion of these structured descriptions with textual and audio descriptors is suitable for the machine-interpretable spatiotemporal annotation of complex video scenes. The resulting structured video annotations can be efficiently queried manually or programmatically, and can be used in scene interpretation, video understanding, and content-based video retrieval.