Image retrieval based on feature weighting and relevance feedback

We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's perception. The basic principle of this work is to give more importance to features with a high likelihood and those which separate well between positive example (PE) classes and negative example (NE) classes. The proposed algorithm was validated separately and in the image retrieval context, and the experiments show that it contributes in improving retrieval effectiveness.