A Two-Stage of Relevance Feedback for Content-Based Image Retrieval

In this paper, an effective relevance feedback (RF) approach is proposed in content-based image retrieval (CBIR). In the first stage, according to the user’s marked images, we get theirs predictive probabilities based-on Bayesian methodology which yields the posteriori of the images in the database; second via justify the weight of elements in each feature extracted of images, we refine features by logistic regression with positive features which get from the first stage. Then we rank the images according to the probability of the images in the database. The retrieval system is repeating until the user is satisfied with the feedback results or the target image has been found. Experimental results are shown to evaluate the method on a large image database in terms of precision and recall.