Overlapping B+trees for temporal data
JCIT Proceedings of the fifth Jerusalem conference on Information technology
Towards a theory of multimedia database systems
Multimedia database systems
Topological relations in the world of minimum bounding rectangles: a study with R-trees
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Foundations of multimedia database systems
Journal of the ACM (JACM)
The advanced video information system: data structures and query processing
Multimedia Systems
Overlapping linear quadtrees: a spatio-temporal access method
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Maintaining knowledge about temporal intervals
Communications of the ACM
Reasoning about Qualitative Spatial Relationships
Journal of Automated Reasoning
Similarity based Retrieval of Pictures Using Indices on Spatial Relationships
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Spatio-Temporal Indexing for Large Multimedia Applications
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Modeling of video spatial relationships in an object database management system
IW-MMDBMS '96 Proceedings of the 1996 International Workshop on Multi-Media Database Management Systems (IW-MMDBMS '96)
Content-based query processing for video databases
IEEE Transactions on Multimedia
BilVideo: A Video Database Management System
IEEE MultiMedia
Spatio-Temporal Querying in Video Databases
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
KiMPA: A Kinematics-Based Method for Polygon Approximation
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Rule-based spatiotemporal query processing for video databases
The VLDB Journal — The International Journal on Very Large Data Bases
Spatio-temporal querying in video databases
Information Sciences—Informatics and Computer Science: An International Journal
An efficient query optimization strategy for spatio-temporal queries in video databases
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
BilVideo: Design and Implementation of a Video Database Management System
Multimedia Tools and Applications
Database research at Bilkent University
ACM SIGMOD Record
BilVideo video database management system
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Automatic detection of salient objects and spatial relations in videos for a video database system
Image and Vision Computing
An intelligent fuzzy object-oriented database framework for video database applications
Fuzzy Sets and Systems
A histogram-based approach for object-based query-by-shape-and-color in image and video databases
Image and Vision Computing
Video model for dynamic objects
Information Sciences: an International Journal
Real-Time Query Processing on Live Videos in Networks of Distributed Cameras
International Journal of Interdisciplinary Telecommunications and Networking
Expert Systems with Applications: An International Journal
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We propose a novel architecture for a video database system incorporating both spatio-temporal and semantic (keyword, event/activity and category-based) query facilities. The originality of our approach stems from the fact that we intend to provide full support for spatio-temporal, relative object-motion and similarity-based objecttrajectory queries by a rule-based system utilizing a knowledge-base while using an object-relational database to answer semantic-based queries. Our method of extracting and modeling spatio-temporal relations is also a unique one such that we segment video clips into shots using spatial relationships between objects in video frames rather than applying a traditional scene detection algorithm. The technique we use is simple, yet novel and powerful in terms of effectiveness and user query satisfaction: video clips are segmented into shots whenever the current set of relations between objects changes and the video frames, where these changes occur, are chosen as keyframes. The directional, topological and third-dimension relations used for shots are those of the keyframes selected to represent the shots and this information is kept, along with frame numbers of the keyframes, in a knowledge-base as Prolog facts. The system has a comprehensive set of inference rules to reduce the number of facts stored in the knowledge-base because a considerable number of facts, which otherwise would have to be stored explicitly, can be derived by rules with some extra effort.