Improving retrieval performance by long-term relevance information
Relevance feedback (RF) is an iterative process which improves the retrieval performance by utilizing the user's feedback on retrieved results. Traditional RF techniques use solely the short-term experience and are short of knowledge of cross-session agreement. In this paper, we propose a novel RF framework which facilitates the combination of short-term and long-term experiences by integrating the traditional methods and a new technique called the virtual feature. The feedback history of all the users is digested by the system and is represented as a virtual feature of the images. As such, the dissimilarity measure can be adapted dynamically depending on the estimate of the relevance probability derived from the virtual features. The results manifest that the proposed framework outperforms the one that adopts a single traditional RF technique.