A Comparison of Relevance Feedback Strategies in CBIR

Relevance feedback (RF) is considered to be very useful in reducing semantic gap and thus enhancing accuracy of a Content-Based Image Retrieval system. In this paper, we have given a brief overview of research done in this area with an emphasis on feature re-weighting approach, a popular RF technique. We have also discussed an instance-based approach that has been introduced very recently. We considered image retrieval as a dichotomous classification problem and compared performances of the two RF strategies with four different datasets, with number of images ranging from 1000 to 19511.