Foundations and Trends in Information Retrieval
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Today's and tomorrow's retrieval practice in the audiovisual archive
Proceedings of the ACM International Conference on Image and Video Retrieval
Utilizing related samples to learn complex queries in interactive concept-based video search
Proceedings of the ACM International Conference on Image and Video Retrieval
Coached active learning for interactive video search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Learning concept bundles for video search with complex queries
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Halfway through the semantic gap: Prosemantic features for image retrieval
Information Sciences: an International Journal
Searching informative concept banks for video event detection
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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This paper describes the construction and utilization of two novel semantic spaces, namely ontology-enriched semantic space (OSS) and ontology-enriched orthogonal semantic space (OS2), to facilitate the selection of concept detectors for video search. These two semantic spaces are enriched with ontology knowledge, while emphasizing consistent and uniform comparison of ontological relatedness among concepts for query-to-concept mapping. OS2, in addition to being a linear space like OSS, also guarantees orthogonality of the semantic space. Compared with other ontology reasoning measures, both spaces are capable of providing platforms that offer a global view of concept inter-relatedness, by allowing evaluation of concept similarity in metric spaces. We simulate OSS and OS2 by using LSCOM concepts and experiment search effectiveness with VIREO-374 concept detectors. Empirical observations indicate that the proposed semantic spaces enable more effective selection of concept detectors than eight other existing ontology measures. OS2, in particular, is better in providing a viable and reasonable solution for fusion of multiple concept detectors.