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The aim of this research is to enhance multimedia retrieval applications by combining both knowledge and statistical data in a learning framework to extract semantic information from multimedia. We will approach the problem as a Bayesian learning problem divided in three parts:
- Multimedia mining: mines the feature space for the problem’s most common patterns and learns the causality relations between the occurrence of these patterns and keywords.
- Multi-modal information fusion: multi-modal features will be combined in a statistical framework to increase the prediction accuracy of keywords in new unseen content. and
- Semantic information extraction: improve the inference results obtained in the previous steps by using knowledge about keywords co-occurrences.