Shape Matching Using a Novel Warping Distance Measure

  • Authors:

  • Yasser Ebrahim;Maher Ahmed;Siu-Cheung Chau;Wegdan Abdelsalam

  • Affiliations:

  • Wilfrid Laurier University, Waterloo, Canada N2L 3C5;Wilfrid Laurier University, Waterloo, Canada N2L 3C5;Wilfrid Laurier University, Waterloo, Canada N2L 3C5;University of Guelph, Guelph, Canada N1G 2W1

  • Venue:
  • ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
  • Year:
  • 2008

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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.