PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Graph-cut transducers for relevance feedback in content based image retrieval
Hichem Sahbi, Jean-Yves Audibert and Renaud Keriven
In: ICCV 2007, 14-20 Oct 2007, Brésil.

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. This RF model exploits the structure of the 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Information Retrieval & Textual Information Access
ID Code:3155
Deposited By:Jean-Yves Audibert
Deposited On:29 December 2007