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Frequency Domain Methods for Content-Based Image Retrieval in Multimedia Databases

  • Chapter
Methods and Supporting Technologies for Data Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 225))

Abstract

Content-based image retrieval is an important application area for image processing methods associated with computer vision, pattern recognition, machine learning and other fields of artificial intelligence. Image content analysis enables us to use more natural, human-level concepts for querying large collections of images typically found in multimedia databases. Out of the numerous features proposed for image content description those based on frequency representation are of special interest as they often offer high levels of invariance to distortions and noise. In this chapter several frequency domain methods designed to describe different aspects of an image, i.e. contour, texture and shape are discussed. Current standards and database solutions supporting content-based image retrieval, including SQL Multimedia and Application Packages, Oracle 9i/10g interMedia and MPEG-7, are also presented.

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Authors and Affiliations

  1. Institute of Computer Science, Technical University of Lodz, ul. Wolczanska 215, 90-924, Lodz, Poland

    Bartłomiej Stasiak & Mykhaylo Yatsymirskyy

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  1. Bartłomiej Stasiak
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  2. Mykhaylo Yatsymirskyy
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  1. Institute of Computer Science, Technical University of Lodz , Wolczanska 215, 90-924, Lodz, Poland

    Danuta Zakrzewska  & Liliana Byczkowska-Lipinska  & 

  2. Facultad de Informatica, Universidad Politecnica de Madrid , Campus de Montegancedo s/n, 28660, Boadilla del Monte Madrid, Spain

    Ernestina Menasalvas

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Stasiak, B., Yatsymirskyy, M. (2009). Frequency Domain Methods for Content-Based Image Retrieval in Multimedia Databases. In: Zakrzewska, D., Menasalvas, E., Byczkowska-Lipinska, L. (eds) Methods and Supporting Technologies for Data Analysis. Studies in Computational Intelligence, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02196-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-02196-1_6

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