Multiple random walk and its application in content-based image retrieval

Jingrui He, Hanghang Tong, Mingjing Li, Ma Wei-Ying, Changshui Zhang

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

7 Scopus citations

Abstract

In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by means of Markov random walks, one for images relevant to the query concept and the other for the irrelevant ones. The goal is to obtain the likelihood functions of both classes. Firstly, MRW generates two random walks with virtual absorbing boundaries, and uses the absorbing probabilities as the initial estimation of the likelihood functions. Then it refines the two random walks through an EMlike iterative procedure in order to get more accurate estimation of the likelihood functions. Class priors are also obtained in this procedure. Finally, MRW ranks all the unlabeled images in the database according to their posterior probabilities of being relevant. By using both labeled and unlabeled data, MRW can be seen as a transductive learning method, which has been demonstrated to outperform inductive ones by previous research work. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of MRW over state-of-the-art techniques.

Original languageEnglish (US)
Title of host publicationMIR 2005 - Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Co-located with ACM Multimedia 2005
PublisherAssociation for Computing Machinery, Inc
Pages151-158
Number of pages8
ISBN (Electronic)1595932445, 9781595932440
DOIs
StatePublished - Nov 10 2005
Event7th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2005 - Singapore, Singapore
Duration: Nov 10 2005Nov 11 2005

Publication series

NameMIR 2005 - Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Co-located with ACM Multimedia 2005

Other

Other7th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2005
CountrySingapore
CitySingapore
Period11/10/0511/11/05

Keywords

  • Class prior
  • Generative model
  • Image retrieval
  • Likelihood function
  • Markov random walk
  • Relevance feedback.

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Signal Processing
  • Software
  • Media Technology

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  • Cite this

    He, J., Tong, H., Li, M., Wei-Ying, M., & Zhang, C. (2005). Multiple random walk and its application in content-based image retrieval. In MIR 2005 - Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Co-located with ACM Multimedia 2005 (pp. 151-158). (MIR 2005 - Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Co-located with ACM Multimedia 2005). Association for Computing Machinery, Inc. https://doi.org/10.1145/1101826.1101852