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Feature Selection and Visual Learning

  • Chapter
Principles of Visual Information Retrieval

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

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Abstract

In many content-based retrieval systems, the user is asked to understand how the computer sees the world. An emerging trend is to try to have the computer understand how people see the world. However, understanding the world is a fundamental computer vision problem which has withstood decades of research. The critical aspect to these emerging methods is that they have modest ambitions. Petkovic [26] has called this finding “simple semantics.” From recent literature, this generally means finding computable image features which are correlated with visual concepts. The key distinction is that we are not trying to fully understand how human intelligence works. This would imply creating a general model for understanding all visual concepts. Instead, we are satisfied to find features which describe some small, but useful domains of visual concepts.

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Authors
  1. Michael S. Lew
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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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Lew, M.S. (2001). Feature Selection and Visual Learning. 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_6

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  • DOI: https://doi.org/10.1007/978-1-4471-3702-3_6

  • 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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