The sparse image representation for automated image retrieval

Praks, Pavel; Kučera, Radek; Izquierdo, Ebroul

doi:10.1109/icip.2008.4711682

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…Although images can be represented very effectively by sparse coefficients based on FFT, the sparsity character of these coefficients is destroyed during the LSI-based dimension reduction process represented by the sparse partial eigenproblem. In our approach, we keep the memory limit of the decomposed data by a statistical model of the sparse data [14]. We successfully used this new sparse approach for a large-scale similarity task in NIST TRECVid 2007 competition as a member of K-Space team [15].…”

Section: Discussionmentioning

confidence: 99%

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

Praks

Svátek

Ĉernohorský

2008

2008 International Workshop on Content-Based Multimedia Indexing

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The surveillance application aims at improving the quality of technology via modelling human expert behaviour in the coking plant ArcelorMittal Ostrava, the Czech Republic. Video data on several industrial processes are captured by means of a CCD camera and classified by using Latent Semantic Indexing (LSI) with the respect to etalons classified by an expert. We also study the convergence behavior of proposed partial eigenproblem-based dimension reduction technique and its ability for knowledge acquisition. Having increased the computational effort of the dimension reduction technique did not imply the increasing quality of retrieved results in our cases.

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Section: Discussionmentioning

confidence: 99%

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

Praks

Svátek

Ĉernohorský

2008

2008 International Workshop on Content-Based Multimedia Indexing

Self Cite

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“…Generally, LSI is a useful tool for the retrieval of similarities in text documents. It has also been used for successful human iris recognition and retrieval images (Praks et al, 2003;2006;2008a;b). Even though feature image (document) vector creation is very similar in both MIA and LSI, the latter does not deal with one document (image) only, but processes many documents simultaneously, arranged as rows in the large document matrix.…”

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confidence: 99%

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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Social Media Modeling and Computing

Hoi¹,

Luo²,

Böll³

et al. 2011

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The sparse image representation for automated image retrieval

Cited by 11 publications

References 10 publications

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

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

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

Social Media Modeling and Computing

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