Automatic Meaningful Video Content Retrieval Based on Conceptual Fuzzy Model

Recent advances in digital video analysis and retrieval have made video more accessible than ever. The representation and recognition of events in a video is important for a number of tasks such as video surveillance, video browsing and content based video indexing. Raw data and low-level features alone are not sufficient to fulfill the user?s needs; that is, a deeper understanding of the content at the semantic level is required.Currently, manual techniques, which are inefficient, subjective and costly in time and limit the querying capabilities.Here, we propose a semantic content extraction system that allows the user to query and retrieve objects, events, and concepts that are extracted automatically. We introduce an ontology-based fuzzy video semantic content model that uses spatial/temporal relations in event and concept definitions. This metaontology definition provides a wide-domain applicable rule construction standard that allows the user to construct ontology for a given domain. In addition to domain ontologies, we use additional rule definitions (without using ontology) to define some complex situations more effectively. The proposed framework has been fully implemented and tested on three different domains and it provides satisfactory results.