A modification of diffusion distance for clustering and image segmentation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86088905" target="_blank" >RIV/61989100:27240/13:86088905 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-02895-8_43#page-1" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-02895-8_43#page-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-02895-8_43" target="_blank" >10.1007/978-3-319-02895-8_43</a>

Result language
angličtina
Original language name
A modification of diffusion distance for clustering and image segmentation
Original language description
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 intoaccount. 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 modific
Czech name
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Czech description
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Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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