Semantic propagation from relevance feedbacks

Abstract

Relevance feedback has been a very useful tool to enhance the performance of content-based information retrieval (CBIR) systems. To fully make use of the precious user feedbacks provided to a system, we propose an approach named semantic propagation, which reveals the deep semantic relationships among objects in the database given a set of relevance feedbacks between object pairs. In particular, we present two semantic propagation algorithms that are applicable to CIBR systems with a feature vector space model and a general metric space model, respectively. Experiments on a 3D model retrieval system and a logo image retrieval system are performed to show the effectiveness of the proposed methods.