1. A method for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the method comprising: obtaining a set of positive feedback images and a set of negative feedback images via relevance feedback, the set of positive feedback images are those images deemed semantically relevant and the set of negative feedback images are those deemed semantically less relevant; within a feature space, moving a positive candidate image towards the set of positive feedback images by adjusting distance metrics of the positive candidate image, the positive candidate image having similar low-level features as those of the set of positive feedback images, within a feature space, distancing a negative candidate image from the set of positive feedback images by adjusting distance metrics of the negative candidate image, the negative candidate image having similar low-level features as those of the set of negative feedback images; constructing a Bayesian classifier of a positive feedback image by positive candidate images.