Ontology-enriched Semantic Space for Video Retrieval

Multimedia-based ontology construction and reasoning have recently been recognized as two important issues in video search, particularly for bridging semantic gap. The lack of coincidence between low-level features and user expectation makes conceptbased ontology reasoning an attractive mid-level framework for interpreting highlevel semantics. In this report, we propose a novel model, namely ontology-enriched semantic space (OSS), to provide a computable platform for modeling and reasoning concepts in a linear space. OSS enlightens the possibility of answering conceptual questions such as a high coverage of semantic space with minimal set of concepts, and the set of concepts to be developed for video search. More importantly, the queryto-concept mapping can be more reasonably conducted by guaranteeing the uniform and consistent comparison of concept scores for video search. We explore OSS for several tasks including concept-based video search, word sense disambiguation and detecor fusion. Our empirical findings show that OSS is a feasible solution to timely issues such as the measurement of concept combination and query-concept dependent fusion.