Manifold-ranking based image retrieval

Jingrui He, Mingjing Li, Hong Jiang Zhang, Hanghang Tong, Changshui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

286 Scopus citations

Abstract

In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance. In relevance feedback, if only positive examples are available, they are added to the query set to improve the retrieval result; if examples of both labels can be obtained, MRBIR discriminately spreads the ranking scores of positive and negative examples, considering the asymmetry between these two types of images. Furthermore, three active learning methods are incorporated into MRBIR, which select images in each round of relevance feedback according to different principles, aiming to maximally improve the ranking result. Experimental results on a general-purpose image database show that MRBIR attains a significant improvement over existing systems from all aspects.

Original languageEnglish (US)
Title of host publicationACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery (ACM)
Pages9-16
Number of pages8
ISBN (Print)1581138938, 9781581138931
DOIs
StatePublished - 2004
EventACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia - New York, NY, United States
Duration: Oct 10 2004Oct 16 2004

Publication series

NameACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia

Other

OtherACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
CountryUnited States
CityNew York, NY
Period10/10/0410/16/04

Keywords

  • Active learning
  • Image retrieval
  • Manifold ranking
  • Relevance feedback

ASJC Scopus subject areas

  • Engineering(all)

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