Hierarchical clustering of RGB surface water images based on MIA-LSI approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F10%3A10224341" target="_blank" >RIV/61989100:27360/10:10224341 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical clustering of RGB surface water images based on MIA-LSI approach

Popis výsledku v původním jazyce

Multivariate image analysis (MIA) combined with the Latent semantic indexing (LSI) method was used for the retrieval of similar water-related images within a testing database of 126 RGB images. This database set up from the digital photographs of variouswater levels and similar images of ground surfaces and plants was transferred into an image matrix, which was treated by principal component analysis (PCA) based on singular value decomposition (SVD). The high dimensionality of original images given bytheir pixels numbers was reduce to six principal components. Thus characterised images were partitioned into clusters of similar images using hierarchical clustering. The best defined clusters were obtained when the Ward?s method was applied. Images werepartitioned into the two main clusters according to the similar colours of displayed objects. Each main cluster was further partitioned into sub-clusters according to the similar shapes and sizes of the objects.

Název v anglickém jazyce

Hierarchical clustering of RGB surface water images based on MIA-LSI approach

Popis výsledku anglicky

Multivariate image analysis (MIA) combined with the Latent semantic indexing (LSI) method was used for the retrieval of similar water-related images within a testing database of 126 RGB images. This database set up from the digital photographs of variouswater levels and similar images of ground surfaces and plants was transferred into an image matrix, which was treated by principal component analysis (PCA) based on singular value decomposition (SVD). The high dimensionality of original images given bytheir pixels numbers was reduce to six principal components. Thus characterised images were partitioned into clusters of similar images using hierarchical clustering. The best defined clusters were obtained when the Ward?s method was applied. Images werepartitioned into the two main clusters according to the similar colours of displayed objects. Each main cluster was further partitioned into sub-clusters according to the similar shapes and sizes of the objects.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/1M06047" target="_blank" >1M06047: Centrum pro jakost a spolehlivost výroby</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název periodika

Water SA

ISSN

0378-4738

e-ISSN

—

Svazek periodika

36

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

ZA - Jihoafrická republika

Počet stran výsledku

7

Strana od-do

—

Kód UT WoS článku

000274194000019

EID výsledku v databázi Scopus

—