Multilevel and Multiple approaches for Feature Reweighting to Reduce Semantic Gap using Relevance Feedback

In this paper, we propose an approach using multilevel and multiple approaches for Feature Reweighting for CBIR system to reduce semantic gap using Relevance feedback. The first step of this approach does analysis on the positive and negative images, Second step calculates normalized feature component sets of images, Third step calculates overall distances between given query image and database images, and the next step calculates Relevance score along with confidence of the image, it is used for Feature Reweighting. All the above methods are performed individually in the previous systems, where as in our propose system we perform all these together. The assumption for the previous relevance feedback systems are that, all the above methods are performed against to the user given feedback. This increases the number of iterations for the retrieval systems. The propose system can do analysis of images, overall distance calculation, automatically calculates the weight of features for an image based on the confidence and score of the relevance before user feedback. And these results are carried forward to the next iteration for further calculations after the user feedback.