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Biological Inspired Methods for Media Classification and Retrieval

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Book cover Multimedia Analysis, Processing and Communications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 346))

Summary

Automatic image clustering and classification is a critical and vibrant research topic in the computer vision community over the last couple of decades. However, the performance of the automatic image clustering and classification tools have been hindered by the commonly referred problem of “Semantic Gap”, which is defined as the gap between low-level features that can be extracted from the media and the high-level semantic concepts humans are able to perceive from media content. Addressing this problem, recent developments in biologically inspired techniques for media retrieval is presented in this chapter.

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Author information

Authors and Affiliations

  1. School of Electronic Engineering and Computer Science, Queen Mary, University of London, UK

    Krishna Chandramouli, Tomas Piatrik & Ebroul Izquierdo

Authors
  1. Krishna Chandramouli
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  2. Tomas Piatrik
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  3. Ebroul Izquierdo
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Editor information

Editors and Affiliations

  1. School of Computer Engineering , Nanyang Technological University, 639798, Singapore

    Weisi Lin  & Dacheng Tao  & 

  2. Intelligent Systems Laboratory Systems Research Institute , Polish Academy of Sciences, Poland

    Janusz Kacprzyk

  3. Department of Computing , Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Zhu Li

  4. School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, U.K.

    Ebroul Izquierdo

  5. TCL-Thomson Electronics , Santa Clara, California

    Haohong Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Chandramouli, K., Piatrik, T., Izquierdo, E. (2011). Biological Inspired Methods for Media Classification and Retrieval. In: Lin, W., Tao, D., Kacprzyk, J., Li, Z., Izquierdo, E., Wang, H. (eds) Multimedia Analysis, Processing and Communications. Studies in Computational Intelligence, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19551-8_3

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  • DOI: https://doi.org/10.1007/978-3-642-19551-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19550-1

  • Online ISBN: 978-3-642-19551-8

  • eBook Packages: EngineeringEngineering (R0)

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