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A Modification of Diffusion Distance for Clustering and Image Segmentation

  • Eduard Sojka
  • Jan Gaura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

Abstract

Measuring the distances is an important problem in many image-segmentation algorithms. The distance should tell whether two image points belong to a single or, respectively, to two different image segments. The simplest approach is to use the Euclidean distance. However, measuring the distances along the image manifold seems to take better into account the facts that are important for segmentation. Geodesic distance, i.e. the shortest path in the corresponding graph or k shortest paths can be regarded as the simplest way how the distances along the manifold can be measured. At a first glance, one would say that the resistance and diffusion distance should provide the properties that are even better since all the paths along the manifold are taken into account. Surprisingly, it is not often true. We show that the high number of paths is not beneficial for measuring the distances in image segmentation. On the basis of analysing the problems of diffusion distance, we introduce its modification, in which, in essence, the number of paths is restricted to a certain chosen number. We demonstrate the positive properties of this new metrics.

Keywords

Image segmentation diffusion distance geodesic distance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Eduard Sojka
    • 1
  • Jan Gaura
    • 1
  1. 1.Faculty of Electrical Engineering and Computer Science, Department of Computer ScienceVŠB - Technical University of OstravaOstrava-PorubaCzech Republic

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