Supporting audiovisual query using dynamic programming
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A multi-modal system for the retrieval of semantic video events
Computer Vision and Image Understanding - Special issue on event detection in video
A framework to enable the semantic inferencing and querying of multimedia content
International Journal of Web Engineering and Technology
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
On supervision and statistical learning for semantic multimedia analysis
Journal of Visual Communication and Image Representation
Semi-automatic semantic tagging of 3D images from pancreas cells
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Understanding how visual context influences multimedia content analysis problems
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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This paper proposes a novel framework for semantic indexing and retrieval in digital video. The components of the framework are probabilistic multimedia objects (multi-objects) and a network of such objects (multi-nets). The main contribution of this paper is a novel application of a factor graph framework to model the interactions in a network of multi-objects (multi-net) at a semantic level. Factor graphs are statistical graphical models that provide an efficient framework for exact and approximate inference via the sum-product algorithm. Incorporating the statistical interactions between the concepts using factor graphs enhances the detection probability of individual multi-objects and provides a unified framework for integrating multiple modalities and supports inference of unobservable concepts based on their relation with observable concepts. Our experiments reveal significant performance improvement using the inference on the factor graph models.