Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
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
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Cluster-based data modeling for semantic video search
Proceedings of the 6th ACM international conference on Image and video retrieval
International Journal of Approximate Reasoning
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMD-based video clip retrieval by many-to-many matching
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Query-Based video event definition using rough set theory and high-dimensional representation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
A quick search method for audio and video signals based on histogram pruning
IEEE Transactions on Multimedia
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In this paper, we develop a method for retrieving events of interest in a video archive. To this end, we address the following two issues. First, due to camera techniques, locations and so on, shots of an event contain significantly different features. So, they cannot be retrieved by a single retrieval model. Thus, we use "rough set theory" to extract multiple classification rules, each of which correctly identifies a subset of shots of the event. Second, although concepts like Person, Car and Cityspace are useful for event retrieval, we need to distinguish between relevant concepts to an event and irrelevant ones. Otherwise, the retrieval performance degrades. So, in order to select concepts relevant to the event, we organize concepts into "video ontology" which is a formal and explicit specification of concepts, concept properties and relations among concepts. Experimental results show both the effectiveness of rough set theory and the one of video ontology.