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Encyclopedia of Database Systems pp 3157–3160Cite as

Relevance Feedback for Content-Based Information Retrieval

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Definition

Relevance feedback (RF) [2, 5] is an on-line approach which tries to learn the user’s intentions on the fly. It leverages users to guide the computers to search for relevant documents. An RF mechanism has two components: a learner and a selector. At every feedback round, the user marks (part of) the images returned by the search engine as relevant or irrelevant. The learner exploits this information to re-estimate the target of the user. This information is used both quantitatively (retrieving more documents like the relevant documents) and qualitatively (retrieving documents similar to the relevant ones before other documents). With the current estimation of the target, the selector chooses other images that are displayed by the interface of the search engine; then the user is asked to provide feedback on these images during the next round. The process of RF is usually presented as a cycle of activity: an IR system presents a user with a set of retrieved documents; the...

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Recommended Reading

  1. Chen Z, Zhu B. Some formal analysis of Rocchio’s similarity-based relevance feedback algorithm. Inf Retr. 2002;5(1):61–86.

    Article  MATH  Google Scholar 

  2. Crucianu M, Ferecatu M, Boujemaa N. Relevance feedback for image retrieval: a short survey. In: State of the art in audiovisual content-based retrieval, Information Universal Access and Interaction, Including Datamodels and Languages. Report of the DELOS2 European Network of Excellence (FP6). (2004).

    Google Scholar 

  3. Liu Y, Zhang D, Lu G, Ma W-Y. A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 2007;40(1):262–82.

    Article  MATH  Google Scholar 

  4. Rui Y, Huang TS, Ortega M, Mehrotra S. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology. 1998;8(5):644–55.

    Article  Google Scholar 

  5. Ruthven I, Lalmas M. A survey on the use of relevance feedback for information access systems, The knowledge engineering review. London: Cambridge University Press; 2003.

    Book  MATH  Google Scholar 

  6. Schölkopf B, Smola A. Learning with kernels. Cambridge, MA: MIT Press; 2002.

    MATH  Google Scholar 

  7. Tian Q, Yu Y, Huang TS. Incorporate discriminant analysis with EM algorithm in image retrieval. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2000. p. 299–302.

    Google Scholar 

  8. Tong S, Chang E. Support vector machine active learning for image retrieval. In: Proceedings of the 9th ACM International Conference on Multimedia; 2001. p. 107–18.

    Google Scholar 

  9. Zhu XS, Huang TS. Relevance feedback in image retrieval: a comprehensive review. Multimedia System. 2003;8(6):536–44.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Xin-Jing Wang & Lei Zhang

  2. Micros Facebook, CA, USA

    Xin-Jing Wang

  3. Microsoft Research, Redmond, WA, USA

    Lei Zhang

Authors
  1. Xin-Jing Wang
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  2. Lei Zhang
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Corresponding author

Correspondence to Xin-Jing Wang .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. The Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

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Wang, XJ., Zhang, L. (2018). Relevance Feedback for Content-Based Information Retrieval. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_308

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