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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F13%3A86088905%21RIV14-MSM-27240___
rdf:type
skos:Concept n17:Vysledek
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-319-02895-8_43#page-1
dcterms: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 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. 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.
dcterms:title
A modification of diffusion distance for clustering and image segmentation A modification of diffusion distance for clustering and image segmentation
skos:prefLabel
A modification of diffusion distance for clustering and image segmentation A modification of diffusion distance for clustering and image segmentation
skos:notation
RIV/61989100:27240/13:86088905!RIV14-MSM-27240___
n17:predkladatel
n18:orjk%3A27240
n3:aktivita
n22:S
n3:aktivity
S
n3:dodaniDat
n14:2014
n3:domaciTvurceVysledku
n8:4442539 n8:4899423
n3:druhVysledku
n21:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n23:predkladatel
n3:idSjednocenehoVysledku
58758
n3:idVysledku
RIV/61989100:27240/13:86088905
n3:jazykVysledku
n20:eng
n3:klicovaSlova
image segmentation; geodesic distance; diffusion distance
n3:klicoveSlovo
n4:image%20segmentation n4:geodesic%20distance n4:diffusion%20distance
n3:kontrolniKodProRIV
[7237C285E199]
n3:mistoKonaniAkce
Poznan
n3:mistoVydani
Berlín
n3:nazevZdroje
Lecture Notes in Computer Science. Volume 8192
n3:obor
n5:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n14:2013
n3:tvurceVysledku
Sojka, Eduard Gaura, Jan
n3:typAkce
n13:WRD
n3:zahajeniAkce
2013-10-28+01:00
s:issn
0302-9743
s:numberOfPages
12
n15:doi
10.1007/978-3-319-02895-8_43
n19:hasPublisher
Springer Heidelberg
n16:isbn
978-3-319-02894-1
n9:organizacniJednotka
27240