A unified framework for semantics and feature based relevance feedback in image retrieval systems

  • Authors:

  • Ye Lu;Chunhui Hu;Xingquan Zhu;HongJiang Zhang;Qiang Yang

  • Affiliations:

  • School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada, V5A1S6;Microsoft Research China, 5F, Beijing Sigma Center, Beijing 100080, China;Department of Computer Science, Fudan University, Shanghai 200433, China;Microsoft Research China, 5F, Beijing Sigma Center, Beijing 100080, China;School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada, V5A1S6

  • Venue:
  • MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
  • Year:
  • 2000

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Abstract

The relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, 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 feedback technique, iFind, to take advantage of the semantic 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.