A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems

摘要

The relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few eyars. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. I naddition, methods that perform optimization on multi-level image content model have been formulated. HOwever, these methods only perform relevance feedback on the low-level image features and fail to address the images' semantic content. In this paper, we propose a relevance feeback technique, iFind, to take advantage of the semanitic contents of the images in addition to the low-level features. By forming a semantic network on top of the keyword association on the images, we are able to accurately deduce and utilize the images' semantic contents for retrieval purposes. The accuracy and effectiveness of our method is demonstrated with experimental results on real-world image collections.