Fuzzy relevance feedback in content-based image retrieval

In this paper, a new notion called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme requires a crisp decision to be made on the relevance of the retrieved images. However, user interpretation varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information request. It is, therefore, inadequate to describe user's fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgment for relevance ranking. A radial basis function (RBF) network with local modeling structure is used for similarity learning. Experimental results show that our system is more user-adaptive, and it can achieve better performance compared with other conventional retrieval systems which are based on hard-decision and global modeling.