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

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Hierarchical clustering of RGB surface water images based on MIA-LSI approach
Original language 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 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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
<a href="/en/project/1M06047" target="_blank" >1M06047: Research Center for Quality and Reliability of Production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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