Long-Term Cross-Session Relevance Feedback Using Virtual Features

Relevance feedback (RF) is an iterative process, which refines the retrievals by utilizing the user's feedback on previously retrieved results. Traditional RF techniques solely use the short-term learning experience and do not exploit the knowledge created during cross sessions with multiple users. In this paper, we propose a novel RF framework, which facilitates the combination of short-term and long-term learning processes by integrating the traditional methods with a new technique called the virtual feature. The feedback history with all the users is digested by the system and is represented in a very efficient form as a virtual feature of the images. As such, the dissimilarity measure can dynamically be adapted, depending on the estimate of the semantic relevance derived from the virtual features. In addition, with a dynamic database, the user's subject concepts may transit from one to another. By monitoring the changes in retrieval performance, the proposed system can automatically adapt the concepts according to the new subject concepts. The experiments are conducted on a real image database. The results manifest that the proposed framework outperforms the traditional within-session and log-based long-term RF techniques.