About: A modification of diffusion distance for clustering and image segmentation

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: A modification of diffusion distance for clustering and image segmentation Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://link.springer.com/chapter/10.1007%2F978-3-319-02895-8_43#page-1
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. (en)
Title	A modification of diffusion distance for clustering and image segmentation A modification of diffusion distance for clustering and image segmentation (en)
skos:prefLabel	A modification of diffusion distance for clustering and image segmentation A modification of diffusion distance for clustering and image segmentation (en)
skos:notation	RIV/61989100:27240/13:86088905!RIV14-MSM-27240___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Sojka, Eduard Gaura, Jan
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / Fakulta elektrotechniky a informatiky
http://linked.open...dnocenehoVysledku	58758
http://linked.open...ai/riv/idVysledku	RIV/61989100:27240/13:86088905
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	image segmentation; geodesic distance; diffusion distance (en)
http://linked.open.../riv/klicoveSlovo	diffusion distance geodesic distance image segmentation
http://linked.open...ontrolniKodProRIV	[7237C285E199]
http://linked.open...v/mistoKonaniAkce	Poznan
http://linked.open...i/riv/mistoVydani	Berlín
http://linked.open...i/riv/nazevZdroje	Lecture Notes in Computer Science. Volume 8192
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Gaura, Jan Sojka, Eduard
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-10-28 (xsd:date)
issn	0302-9743
number of pages	12 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-02895-8_43
http://purl.org/ne...btex#hasPublisher	Springer Heidelberg
https://schema.org/isbn	978-3-319-02894-1
http://localhost/t...ganizacniJednotka	27240

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 38 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software