Linear algebra for vision-based surveillance in heavy industry - convergence behavior case study

Praks, Pavel; Svátek, Vojtěch; Ĉernohorský, Jindřich

doi:10.1109/cbmi.2008.4564967

Cited by 1 publication

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References 13 publications

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“…They clearly demonstrate that an increase in k leads to the worst cluster separation. The decrease of similar images retrieval efficiency with increasing k value has also been found by Praks et al (2008b). The dendrograms in Figs.…”

Section: Clustering Of 256 X 192 Pixels Imagessupporting

confidence: 71%

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

Praus¹,

Praks²

2010

WSA

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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, compiled from digital photographs of the various water levels and similar images of surface areas and vegetation, was transferred into an image matrix, and reorganised by means of principal component analysis (PCA) based on singular value decomposition (SVD). The high dimensionality of original images given by their pixel numbers was reduced to 6 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 2 main clusters in terms of similar colours of displayed objects. Each main cluster was further partitioned into sub-clusters according to similar shapes and sizes of the objects. The clustering results were verified by the visual comparison of selected images. It was found that the MIA-LSI approach complemented with a suitable clustering method is able to recognise the similar images of surface water according to the colour and shape of floating subjects. This finding can be utilised for the automatic computeraided visual monitoring of surface water quality by means of digital images.

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Section: Clustering Of 256 X 192 Pixels Imagessupporting

confidence: 71%