Abstract | ||
---|---|---|
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. |
Year | DOI | Venue |
---|---|---|
2006 | 10.1007/978-3-540-71545-0_3 | Adaptive Multimedia Retrieval |
Keywords | Field | DocType |
visual data,video clip,video database model,low-level representative feature,ontology-supported video modeling,automatic object,low-level feature,reliable video data model,current solution,low-level feature value,concept extraction,data model | Video modeling,Data mining,Ontology,Information retrieval,Database model,Computer science,Bridging (networking),Fuzzy logic,Video tracking,Concept extraction,Data model | Conference |
Volume | ISSN | Citations |
4398 | 0302-9743 | 2 |
PageRank | References | Authors |
0.38 | 13 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yakup Yildirim | 1 | 32 | 3.00 |
Adnan Yazici | 2 | 649 | 56.29 |