Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data modelling versus ontology engineering
ACM SIGMOD Record
Spatio-Temporal Querying in Video Databases
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Semantic retrieval of multimedia data
Proceedings of the 2nd ACM international workshop on Multimedia databases
An Ontology for Video Event Representation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
STRG-Index: spatio-temporal region graph indexing for large video databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A physical model-based approach to detecting sky in photographic images
IEEE Transactions on Image Processing
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
An ontology-based approach of multimedia information personalized search
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
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Current solutions are still far from reaching the ultimate goal, namely to enable users to retrieve the desired video clip among massive amounts of visual data in a semantically meaningful manner. With this study we propose a video database model that provides nearly automatic object, event and concept extraction. It provides a reasonable approach to bridging the gap between low-level representative features and high-level semantic contents from a human point of view. By using training sets and expert opinions, low-level feature values for objects and relations between objects are determined. At the top level we have an ontology of objects, events and concepts. Objects and/or events use all these information to generate events and concepts. The system has a reliable video data model, which gives the user the ability to make ontology-supported fuzzy querying. Queries containing objects, events, spatio-temporal clauses, concepts and low-level features can be handled.