TY - GEN
T1 - Ontology-supported video modeling and retrieval
AU - Yildirim, Yakup
AU - Yazici, Adnan
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38049144437&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38049144437&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71545-0_3
DO - 10.1007/978-3-540-71545-0_3
M3 - Conference contribution
AN - SCOPUS:38049144437
SN - 9783540715443
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 41
BT - Adaptive Multimedia Retrieval
PB - Springer Verlag
T2 - 4th International Workshop on Adaptive Multimedia Retrieval, AMR 2006
Y2 - 27 July 2006 through 28 July 2006
ER -