Performance Evaluation of Dimensionality Reduction Techniques for Multispectral Images

摘要

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.