Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Relevance Feedback for Content-Based Information Retrieval

  • Xin-Jing Wang
  • Lei Zhang
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_308

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...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Chen Z, Zhu B. Some formal analysis of Rocchio’s similarity-based relevance feedback algorithm. Inf Retr. 2002;5(1):61–86.zbMATHCrossRefGoogle Scholar
  2. 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. 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.zbMATHCrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.zbMATHCrossRefGoogle Scholar
  6. 6.
    Schölkopf B, Smola A. Learning with kernels. Cambridge, MA: MIT Press; 2002.zbMATHGoogle Scholar
  7. 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. 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. 9.
    Zhu XS, Huang TS. Relevance feedback in image retrieval: a comprehensive review. Multimedia System. 2003;8(6):536–44.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Microsoft Research AsiaBeijingChina
  2. 2.Micros FacebookCAUSA
  3. 3.Microsoft ResearchRedmondUSA

Section editors and affiliations

  • Jeffrey Xu Yu
    • 1
  1. 1.The Chinese University of Hong KongHong KongChina