Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Estimating average precision with incomplete and imperfect judgments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Building a comprehensive ontology to refine video concept detection
Proceedings of the international workshop on Workshop on multimedia information retrieval
Refining video annotation by exploiting pairwise concurrent relation
Proceedings of the 15th international conference on Multimedia
Multi-cue fusion for semantic video indexing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Exploring inter-concept relationship with context space for semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Using visual context and region semantics for high-level concept detection
IEEE Transactions on Multimedia - Special issue on integration of context and content
Semantic video indexing by fusing explicit and implicit context spaces
Proceedings of the international conference on Multimedia
Incorporating Concept Ontology for Hierarchical Video Classification, Annotation, and Visualization
IEEE Transactions on Multimedia
Multi-Layer Multi-Instance Learning for Video Concept Detection
IEEE Transactions on Multimedia
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
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The context-based concept fusion (CBCF) is increasingly used in video semantic indexing, which uses various relations among different concepts to refine the original detection results. In this paper, we present a CBCF method called Temporal-Spatial Node Balance algorithm (TSNB). This method is based on a physical model, in which the concepts are regard as nodes and the relations are regard as forces. Then all the spatial and temporal relations and the moving cost of the nodes will be balanced. This method is intuitive and observable to explain a concept how to influence others or be influenced by others. And it uses both the spatial and temporal information to describe the semantic structure of the video. We use TSNB algorithm on the datasets of TRECVid 2005-2010. The results show that this method outperforms all the existed works as we know. Besides, it is faster.