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

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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 various water 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 by their 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 were partitioned 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. 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 various water 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 by their 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 were partitioned 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. (en)
Title	Hierarchical clustering of RGB surface water images based on MIA-LSI approach Hierarchical clustering of RGB surface water images based on MIA-LSI approach (en)
skos:prefLabel	Hierarchical clustering of RGB surface water images based on MIA-LSI approach Hierarchical clustering of RGB surface water images based on MIA-LSI approach (en)
skos:notation	RIV/61989100:27360/10:10224341!RIV11-MSM-27360___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(1M06047)
http://linked.open...iv/cisloPeriodika	1
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Praus, Petr
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
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 metalurgie a materiálového inženýrství
http://linked.open...dnocenehoVysledku	261481
http://linked.open...ai/riv/idVysledku	RIV/61989100:27360/10:10224341
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Multivariate image analysis, Latent semantic indexing, RGB image, Wards clustering, water quality (en)
http://linked.open.../riv/klicoveSlovo	RGB image water quality Latent semantic indexing Multivariate image analysis Wards clustering
http://linked.open...odStatuVydavatele	ZA - Jihoafrická republika
http://linked.open...ontrolniKodProRIV	[541D5C6A9F00]
http://linked.open...i/riv/nazevZdroje	Water SA
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Research Center for Quality and Reliability of Production
http://linked.open...UplatneniVysledku	2010
http://linked.open...v/svazekPeriodika	36
http://linked.open...iv/tvurceVysledku	Praus, Petr Praks, Pavel
http://linked.open...ain/vavai/riv/wos	000274194000019
issn	0378-4738
number of pages	7 (xsd:int)
http://localhost/t...ganizacniJednotka	27360

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