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An empirical study of inter-concept similarities in multimedia ontologies
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Correlative multi-label video annotation
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Cross-domain video concept detection using adaptive svms
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Video search re-ranking via multi-graph propagation
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Ontology-enriched semantic space for video search
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Semantic concept-based query expansion and re-ranking for multimedia retrieval
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A comparison of color features for visual concept classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Rich metadata and context capturing through CIDOC/CRM and MPEG-7 interoperability
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Probabilistic optimized ranking for multimedia semantic concept detection via RVM
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Performance evaluation of relevance feedback methods
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A probabilistic ranking framework using unobservable binary events for video search
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Correlative multilabel video annotation with temporal kernels
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Image categorization combining neighborhood methods and boosting
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Large margin Boltzmann machines
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Hi-index | 0.01 |
We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that affect performance of multimedia analysis methods, while at the same time fostering repeatability of experiments. To arrive at a challenge problem, we provide a general scheme for the systematic examination of automated concept detection methods, by decomposing the generic video indexing problem into 2 unimodal analysis experiments, 2 multimodal analysis experiments, and 1 combined analysis experiment. For each experiment, we evaluate generic video indexing performance on 85 hours of international broadcast news data, from the TRECVID 2005/2006 benchmark, using a lexicon of 101 semantic concepts. By establishing a minimum performance on each experiment, the challenge problem allows for component-based optimization of the generic indexing issue, while simultaneously offering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that inuence video indexing performance, the challenge offers to the research community a manually annotated concept lexicon, pre-computed low-level multimedia features, trained classifier models, and five experiments together with baseline performance, which are all available at http://www.mediamill.nl/challenge/.