Fusing semantics, observability, reliability and diversity of concept detectors for video search

Effective utilization of semantic concept detectors for large-scale video search has recently become a topic of intensive studies. One of main challenges is the selection and fusion of appropriate detectors, which considers not only semantics but also the reliability of detectors, observability and diversity of detectors in target video domains. In this paper, we present a novel fusion technique which considers different aspects of detectors for query answering. In addition to utilizing detectors for bridging the semantic gap of user queries and multimedia data, we also address the issue of "observability gap" among detectors which could not be directly inferred from semantic reasoning such as using ontology. To facilitate the selection of detectors, we propose the building of two vector spaces: semantic space (SS) and observability space (OS). We categorize the set of detectors selected separately from SS and OS into four types: anchor, bridge, positive and negative concepts. A multi-level fusion strategy is proposed to novelly combine detectors, allowing the enhancement of detector reliability while enabling the observability, semantics and diversity of concepts being utilized for query answering. By experimenting the proposed approach on TRECVID 2005-2007 datasets and queries, we demonstrate the significance of considering observability, reliability and diversity, in addition to the semantics of detectors to queries.