Encyclopedia of Database Systems

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

Image Retrieval and Relevance Feedback

  • Michel CrucianuEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1016


Relevance feedback is a means of refining a query in an information retrieval system by asking the user to specify how relevant each result of the query is. An image retrieval session relying on relevance feedback is interactive and iterative. The session is divided into several consecutive rounds. At every round, the user provides feedback regarding the current retrieval results, usually by qualifying the returned images as either “relevant” or “irrelevant”; from this feedback, the system attempts to better identify the target of the user and to return improved results. A relevance feedback mechanism should maximize the relevance of the results while minimizing the amount of interaction between the user and the system.

Historical Background

In the early years of content-based image retrieval (CBIR), query by visual example (QBVE) was a prevailing paradigm. To support QBVE, an image retrieval system must first extract, during an off-line phase, a description in terms of...

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

Authors and Affiliations

  1. 1.Conservatoire National des Arts et MétiersParisFrance