Dynamic feature weights with relevance feedback in content-based image retrieval

In this paper, we present a novel relevance feedback method for content-based image retrieval systems based on dynamic feature weights. The proposed method utilizes intra-cluster and inter-cluster information for representing the descriptive and discriminative properties of the features according to the labeled images by the user. Afterwards, feature weights are updated dynamically according to the user's preferences for improving retrieval results. The proposed method has been thoroughly evaluated and selected results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with small number of iterations and labeled samples. Furthermore, it is a low-complex and flexible method that can be used on various databases and content-based image retrieval applications.