Ontology-supported object and event extraction with a genetic algorithms approach for object classification

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.

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