Matrix analysis
Algorithms for clustering data
Algorithms for clustering data
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Semantic representation: search and mining of multimedia content
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
An empirical study of inter-concept similarities in multimedia ontologies
Proceedings of the 6th ACM international conference on Image and video retrieval
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search in concept subspace: a text-like paradigm
Proceedings of the 6th ACM international conference on Image and video retrieval
Ontology-enriched semantic space for video search
Proceedings of the 15th international conference on Multimedia
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
IEEE Transactions on Multimedia
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
Collective Evolutionary Indexing of Multimedia Objects
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
Semantic context transfer across heterogeneous sources for domain adaptive video search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Exploring inter-concept relationship with context space for semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Effectiveness of video ontology in query by example approach
AMT'11 Proceedings of the 7th international conference on Active media technology
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
Constructing and Utilizing Video Ontology for Accurate and Fast Retrieval
International Journal of Multimedia Data Engineering & Management
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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.