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Object driven semantic multi-video summarisation based on ontology

Multi-Video summarisation frameworks aim to extract representative frames from a collection of videos. In the proposed framework, a multi-video summarisation model is implemented that detects key frames from a collection of related videos on the basis of user query object and ontology inference approach. The framework also develops a novel large-scale ontology for video genre identification based on the characteristics of genre-specific videos. The presence of ontologies aids in generating semantically relevant summaries compared to traditional approaches. Quantitative evaluation ensures that maximum information from the video collection on the basis of query is retrieved. Additionally, the ontology-based query inference approach reduces the computation time significantly. Qualitative results prove that the summary generated is concise, informative and semantically relevant.