Article,
Learning a semantic space from user's relevance feedback for image retrieval
Circuits and Systems for Video Technology, IEEE Transactions on, 13 (1): 39--48 (2003)
DOI: http://dx.doi.org/10.1109/TCSVT.2002.808087
Abstract
As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.
