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Performance evaluation of dimensionality reduction techniques for multispectral images
Author(s) -
Carmona Pedro Latorre,
Lenz Reiner
Publication year - 2007
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20107
Subject(s) - isomap , multispectral image , dimensionality reduction , principal component analysis , computer science , artificial intelligence , nonlinear dimensionality reduction , daylight , pattern recognition (psychology) , laplace operator , identification (biology) , computer vision , reduction (mathematics) , mathematics , optics , physics , geometry , botany , biology , mathematical analysis
We consider several collections of multispectral color signals and describe how linear and nonlinear methods can be used to investigate their internal structure. We use databases consisting of blackbody radiators, approximated and measured daylight spectra, multispectral images of indoor and outdoor scenes under different illumination conditions, and numerically computed color signals. We apply principal components analysis, group‐theoretical methods and three manifold learning methods: Laplacian Eigenmaps, ISOMAP, and conformal component analysis. Identification of low‐dimensional structures in these databases is important for analysis, model building and compression and we compare the results obtained by applying the algorithms to the different databases. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 202–217, 2007