Learning and inferring a semantic space from user's relevance feedback for image retrieval

  • Authors:

  • Xiaofei He;Wei-Ying Ma;Oliver King;Mingjing Li;Hongjiang Zhang

  • Affiliations:

  • University of Chicago;Microsoft Research Asia, Beijing, China;University of California at Berkeley;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the tenth ACM international conference on Multimedia
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

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 the 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.