A modification of diffusion distance for clustering and image segmentation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A modification of diffusion distance for clustering and image segmentation

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

A modification of diffusion distance for clustering and image segmentation

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 8192

ISBN

978-3-319-02894-1

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

480-491

Název nakladatele

Springer Heidelberg

Místo vydání

Berlín

Místo konání akce

Poznan

Datum konání akce

28. 10. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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