On SVD-free latent semantic indexing for image retrieval for application in a hard industrial environment

Praks, Pavel; Snášel, Václav; Dvorský, Jiří; Ĉernohorský, Jindřich

doi:10.1109/icit.2003.1290365

Cited by 13 publications

(11 citation statements)

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“…Recently, the methods of numerical linear algebra, espe- cially SVD, have also been successfully used for diverse applications such as general image retrieval [8,9], face recognition and reconstruction [7], iris recognition [11], information retrieval in hydrochemical data [12], and even as an support for information extraction from HTML product catalogues [6]. A comparison of two approaches for classification of metallography images from a steel plant is presented in [13].…”

Section: Principles Of Lsimentioning

confidence: 99%

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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: Principles Of Lsimentioning

confidence: 99%

“…In our approach [8,9,10,11], a raster image is coded as a sequence of pixels. Then the coded image can be understood as a vector of a m-dimensional space, where m denotes the number of pixels (attributes).…”

Section: Image Codingmentioning

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 results in [1], [7], [8], [9] and [6] indicate that LSA can perform matching based on semantic content. The game matrix is analyzed by LSA and semantic information about game is obtained.…”

Section: Strategy Extraction Methodsmentioning

confidence: 99%

“…The SVD is commonly used in solution of unconstrained linear least squares problems, matrix rank estimation, and canonical correlation analysis [8]. The singular value decomposition takes a rectangular m ×n matrix A and calculates three matrices U, S, and V. S is a diagonal m ×n matrix (the same dimensions as A).…”

Section: Strategy Extraction Methodsmentioning

confidence: 99%

Strategy Extraction for Mobile Embedded Control Systems Apply the Multi-agent Technology

Srovnal

Horak

Bernatík

et al. 2004

Computational Science - ICCS 2004

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Abstract. Mobile embedded systems belong among the typical applications of the distributed systems control in real time. An example of a mobile control system is the robotic system. The proposal and realization of such distributed control system represents a demanding and complex task of real time control. In the process of robot soccer game application extensive data is accumulated. A reduction of such data is a possible win in game strategy. SVD decomposition of matrixes is used in data reduction.

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“…where U (nxn) and V T (pxp) are orthogonal and normalized matrices, i.e., U SVD has already found the wide range of various applications in molecular dynamic and gene expression analysis [7], information retrieval, e.g., in the technique of Latent Semantic Indexing [8], image processing [9], spectral analysis [10], etc.…”

Section: Introductionmentioning

confidence: 99%

SVD-based principal component analysis of geochemical data

Praus

2005

Open Chemistry

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Principal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2 % of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration. The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.

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On SVD-free latent semantic indexing for image retrieval for application in a hard industrial environment

Cited by 13 publications

References 1 publication

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

Strategy Extraction for Mobile Embedded Control Systems Apply the Multi-agent Technology

SVD-based principal component analysis of geochemical data

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