Maintaining knowledge about temporal intervals
Communications of the ACM
IEEE Transactions on Knowledge and Data Engineering
Reasoning about Gradual Changes of Topological Relationships
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
Modeling of Moving Objects in a Video Database
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Machine Learning
Travel Ontology for Intelligent Recommendation System
AMS '09 Proceedings of the 2009 Third Asia International Conference on Modelling & Simulation
Ontology reasoning in the SHOQ(D) description logic
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Similarity retrieval of videos by using 3D C-string knowledge representation
Journal of Visual Communication and Image Representation
Semantic video annotation by mining association patterns from visual and speech features
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Extended spatio-temporal relations between moving and non-moving objects
ARES'11 Proceedings of the IFIP WG 8.4/8.9 international cross domain conference on Availability, reliability and security for business, enterprise and health information systems
| Hi-index | 0.01 |
In Video data, moving object has temporal flow and spatial features, which can be expressed in spatio-temporal relation. The goal in this paper is the understanding of object movement of spatio-temporal relation through mapping between vocabulary and object movement. In this case, spatio-temporal relation consists of temporal relation obedient to the passage of time, directional relation obedient to changes of object movement direction, changes of object size relation, topological relation obedient to changes of object movement position, and velocity relation using concept relations between topology models. This paper in the ontology building defines the inference rules using the proposed spatio-temporal relation and the use of Markov Logic Networks for probabilistic reasoning. In the experiments, motion verbs are used to understand semantic object movement. Probability weight and learning for 10,000 times are used for value comparison. The result value from inference exists even though connection rules such as ''go through'' are not defined directly. In addition, it is indicated that the relation that includes large number of connections such as ''go to'' has the high value of probabilistic inference result and that small number of connection relations depending on the change in object size such as ''include'' leads to low value of result.