An Enhanced Query Model for Soccer Video Retrieval Using Temporal Relationships
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A unified framework for semantic shot representation of sports video
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Learning rich semantics from news video archives by style analysis
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Toward a Common Event Model for Multimedia Applications
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Classification of video events using 4-dimensional time-compressed motion features
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Multimodal semantic analysis and annotation for basketball video
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Unsupervised pattern mining from symbolic temporal data
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An event detection framework in video sequences based on hierarchic event structure perception
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Active post-refined multimodality video semantic concept detection with tensor representation
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Learning ontology rules for semantic video annotation
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Sports event detection using temporal patterns mining and web-casting text
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Multi-modality video shot clustering with tensor representation
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Evaluation of video news classification techniques for automatic content personalisation
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Query-based video event definition using rough set theory
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Video news classification for automatic content personalization: a genetic algorithm based approach
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A new spatio-temporal method for event detection and personalized retrieval of sports video
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Soccer video event detection by fusing middle level visual semantics of an event clip
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A comprehensive study of visual event computing
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Event retrieval in video archives using rough set theory and partially supervised learning
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Query-Based video event definition using rough set theory and high-dimensional representation
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HMM based soccer video event detection using enhanced mid-level semantic
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Time matters!: capturing variation in time in video using fisher kernels
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We propose the time interval multimedia event (TIME) framework as a robust approach for classification of semantic events in multimodal video documents. The representation used in TIME extends the Allen temporal interval relations and allows for proper inclusion of context and synchronization of the heterogeneous information sources involved in multimodal video analysis. To demonstrate the viability of our approach, it was evaluated on the domains of soccer and news broadcasts. For automatic classification of semantic events, we compare three different machine learning techniques, i.c. C4.5 decision tree, maximum entropy, and support vector machine. The results show that semantic video indexing results significantly benefit from using the TIME framework.