Multimodal image retrieval via bayesian information fusion
Rui Zhang, Ling Guan · 2009
In this paper, a multimodal image retrieval framework integrating the information in both audio and visual domain via Bayesian decision level fusion is proposed. In both domains, a statistical model for each semantic class is learned. Based on the Bayes' theorem, the a posteriori probability of each class given a query is calculated in the audio domain, which is propagated to the images classified into the corresponding semantic class in the visual domain. These probabilistic measures are utilized as the a priori probability in the overall framework, which is combined with the likelihood evaluated based on nearest neighbor content-based image retrieval. Through the Bayes' theorem again, the images are ranked based on their a posteriori probabilities given the audio and visual feature of a query. To further improve the system, we also propose a relevance feedback scheme in the audio domain. Experimental results demonstrate the advantage of the proposed method over the retrieval simply based on visual features.