Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval

Abstract

Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of questions and answers (Q&A). The user's feedback to these questions provides a CBIR system with a partial labeling of the data and makes it possible to iteratively refine a decision rule on the unlabeled data. Training of this decision rule is referred to as transductive learning. This work is an original approach to relevance feedback (RF) based on graph-cuts. Training consists in implicitly modeling the manifold enclosing both the labeled and unlabeled dataset and finding a partition of this manifold using a min-cut. The contribution of this work is two-fold (i) this is the first comprehensive study of relevance feedback using graph cuts and (ii) our RF model exploits the structure of the data manifold by considering also the structure of the unlabeled data. Experiments conducted on generic as well as specific databases show that our graph-cut based approach is very effective, outperforms other existing methods and makes it possible to converge to almost all the images of the user's "class of interest" with a very small labeling effort. A demo is available through our image retrieval tool kit (IRTK).