Long Term Relevance Feedback: A Probabilistic Axis Re-Weighting Update Scheme

Content Based Retrieval (CBR) systems use Relevance Feedback (RF) to fill the semantic gap. RF can be short-term or long-term. The introduction of long-term learning methods address the memory problem in short-term learning methods. In this letter we propose a new method to enhance the gain of long-term relevance feedback. We have come up with a long term learning scheme in relevance feedback for CBR. The proposed system integrates the user feedback from all iterationations and instills memory into the feedback system of CBR without saving any log of earlier retrievals. In this letter, we have come up with a method to update the cluster parameters and weights assigned to features by accumulating the knowledge obtained from the user over iterations. The proposed update method is validated in the image retrieval context in terms of conventional recall-precision graph and retrieval accuracy.