Autonomous document classification for business
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
What Are Ontologies, and Why Do We Need Them?
IEEE Intelligent Systems
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
A Semantic Web Primer
Semantic retrieval of multimedia data
Proceedings of the 2nd ACM international workshop on Multimedia databases
Ontology-Based Semantic Indexing for MPEG-7 and TV-Anytime Audiovisual Content
Multimedia Tools and Applications
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
ClassView: hierarchical video shot classification, indexing, and accessing
IEEE Transactions on Multimedia
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 incremental genetic algorithm for classification and sensitivity analysis of its parameters
Expert Systems with Applications: An International Journal
Flexible content extraction and querying for videos
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
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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 (OVDAM) that provides automatic object, event and concept extraction. By using training sets and expert opinions, low-level feature values for objects and relations between objects are determined. N-Cut image segmentation algorithm is used to determine segments in video keyframes and the genetic algorithm-based classifier is used to make classification of segments (candidate objects) to objects. At the top level ontology of objects, events and concepts are used. 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. RDF is used to represent metadata. OWL is used to represent ontology and RDQL is used for querying. Queries containing objects, events, spatio-temporal clauses, concepts and low-level features are handled.