A collaborative Bayesian image retrieval framework

In this paper, an image retrieval framework combining content-based and content-free methods is proposed, which employs both short-term relevance feedback (STRF) and long-term relevance feedback (LTRF) as the means of user interaction. The STRF refers to iterative query-specific model learning during a retrieval session, and the LTRF is the estimation of a user history model from the past retrieval results approved by previous users. The framework is formulated based on the Bayes´ theorem, in which the results from STRF and LTRF play the roles of refining the likelihood and the a priori information, respectively, and the images are ranked according to the a posteriori probability. Since the estimation of the user history model is based on the principle of collaborative filtering, the system is referred to as a collaborative Bayesian image retrieval (CLBIR) framework. To evaluate the effectiveness of the proposed framework, nearest neighbor CLBIR (NN-CLBIR) and support vector machine active learning CLBIR (SVMAL-CLBIR) were implemented. Experimental results showed the improvement over content-based methods in terms of both accuracy and ranking due to the integration in the proposed framework.