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Improving Shape-Based CBIR for Natural Image Content Using a Modified GFD

  • Conference paper
Image Analysis and Recognition (ICIAR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3656))

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Abstract

We present a modified version of the Generic Fourier Descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduce the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise tend to contaminate the shape feature description. We find that the proposed techniques not only improve overall retrieval in terms of shape, but more importantly, provide for a more accurate similarity ranking of retrieved results, demonstrating greater consideration for dominant internal and external shape details.

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References

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Author information

Authors and Affiliations

  1. Multimedia Research Laboratory, Ryerson University, Toronto, ON, Canada

    Yupeng Li & Ling Guan

  2. School of Electrical & Information Engineering, University of Sydney, NSW, Australia

    Matthew J. Kyan

Authors
  1. Yupeng Li
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  2. Matthew J. Kyan
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  3. Ling Guan
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, P.O. Box, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2005 Springer-Verlag Berlin Heidelberg

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Li, Y., Kyan, M.J., Guan, L. (2005). Improving Shape-Based CBIR for Natural Image Content Using a Modified GFD. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_73

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  • DOI: https://doi.org/10.1007/11559573_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29069-8

  • Online ISBN: 978-3-540-31938-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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