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Shape Matching Using a Novel Warping Distance Measure

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Image Analysis and Recognition (ICIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5112))

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Abstract

This paper presents a novel distance measure, the Minimum Landscape Distance (MLD). MLD is a warping distance measure that provides a non-linear mapping between the elements in one sequence to those of another. Each element in one sequence is mapped to that with the highest neighborhood structural similarity (landscape) in the other sequence within a window. Different window sizes are tested on a number of datasets and a linear relationship between the window size and the sequence size is discovered. Experimental results obtained on the Kimia-99 and Kimia-216 datasets show that MLD is superior to the Euclidean, correlation, and Dynamic Time Warping (DTW) distance measures.

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Authors and Affiliations

  1. Wilfrid Laurier University, Waterloo, ON, N2L 3C5, Canada

    Yasser Ebrahim, Maher Ahmed & Siu-Cheung Chau

  2. University of Guelph, Guelph, ON, N1G 2W1, Canada

    Wegdan Abdelsalam

Authors
  1. Yasser Ebrahim
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  2. Maher Ahmed
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  3. Siu-Cheung Chau
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  4. Wegdan Abdelsalam
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Editor information

Aurélio Campilho Mohamed Kamel

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© 2008 Springer-Verlag Berlin Heidelberg

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Ebrahim, Y., Ahmed, M., Chau, SC., Abdelsalam, W. (2008). Shape Matching Using a Novel Warping Distance Measure. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_46

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  • DOI: https://doi.org/10.1007/978-3-540-69812-8_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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