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Principles of Visual Information Retrieval pp 219–258Cite as

Relevance Feedback Techniques in Image Retrieval

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Part of the book series: Advances in Pattern Recognition ((ACVPR))

Abstract

Despite the extensive research effort, the retrieval techniques used in content-based image retrieval (CBIR) systems lag behind the corresponding techniques in today’s best text search engines, such as Inquery [2], Alta Vista, and Lycos. One reason is that the information embedded in an image is far more complex than that in text. To better understand the history and methodology of CBIR and how we can improve CBIR’s performance, we will first introduce an image object model before we go into the details of the discussions. An image object (O) can be modeled as a function of the image data (D), features (F), and representations (R). This is described below and also shown in Fig. 9.1:

$$O = O(D,F,R).$$
(9.1)

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Authors
  1. Yong Rui
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  2. Thomas S. Huang
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Editor information

Editors and Affiliations

  1. Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands

    Michael S. Lew PhD

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© 2001 Springer-Verlag London

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Rui, Y., Huang, T.S. (2001). Relevance Feedback Techniques in Image Retrieval. In: Lew, M.S. (eds) Principles of Visual Information Retrieval. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-3702-3_9

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  • DOI: https://doi.org/10.1007/978-1-4471-3702-3_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-868-3

  • Online ISBN: 978-1-4471-3702-3

  • eBook Packages: Springer Book Archive

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