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
VideoQ: an automated content based video search system using visual cues
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Maintaining knowledge about temporal intervals
Communications of the ACM
OVID: Design and Implementation of a Video-Object Database System
IEEE Transactions on Knowledge and Data Engineering
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)
KiMPA: A Kinematics-Based Method for Polygon Approximation
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Plane-based object categorisation using relational learning
Machine Learning
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In this paper, we propose a new approach for high level segmentation of a video clip into shots using spatio-temporal relationships between objects in video frames. 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 have occurred are chosen as key frames. The topological and directional relations used for shots are those of the key frames that have been selected to represent shots and this information is kept, along with key frame intervals, in a knowledge-base as Prolog facts. We also have a comprehensive set of inference rules in order to reduce the number of facts stored in our knowledge-base because a considerable number of facts, which otherwise would have to be stored explicitly, can be derived by these rules with some extra effort.