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Mix and Match Features in the ImageRover Search Engine

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Principles of Visual Information Retrieval

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

Abstract

Ideally, an image search engine should support a broad range of possible queries, topics, users, and similarity measures. To date, the general approach in most image search engines has been to deploy an arsenal of modules that compute a broad variety of image decompositions and discriminants: color histograms, edge orientation histograms, texture measures, shape invariants, eigen decompositions, wavelet coefficients, text cues extracted from the text surrounding the image in the WWW document, etc. These features are precomputed during a WWW crawl, and stored for subsequent queries.

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Authors
  1. Stan Sclaroff
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  2. Marco La Cascia
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  3. Saratendu Sethi
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  4. Leonid Taycher
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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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Sclaroff, S., La Cascia, M., Sethi, S., Taycher, L. (2001). Mix and Match Features in the ImageRover Search Engine. 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_10

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  • DOI: https://doi.org/10.1007/978-1-4471-3702-3_10

  • 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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