Illuminant dependence of PCA, NMF and NTF in spectral color imaging

In this study principal component analysis (PCA), non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) are applied as dimension reduction methods in color spectrum domain. The effect of light sources to the quality of the reconstructed spectrum is investigated. Due to the orthogonality, the corresponding bases vectors from PCA usually contain negative coefficients and are difficult to implement optically. NTF and NMF find non-negative basis for color spectrum space and are more suitable for optical implementation. Also the energy of NMF and NTF bases is more concentrated than PCA one. They should be more suitable for peaky light sources (i.e. Poly lux or LED) Five reflectance spectra sets from the different sources were used in tests. Four light source spectrums with the various shapes were applied for light source simulation. We evaluate reconstruction spectrum by quality and error measures including AE, GFC and PSNR.

[1]  T. W. Anderson An Introduction to Multivariate Statistical Analysis , 1959 .

[2]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[3]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[4]  J. Hernández-Andrés,et al.  Colorimetric and spectroradiometric characteristics of narrow-field-of-view clear skylight in Granada, Spain. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  A. Kaarna Sparse Coded Spatial Features from Spectral Images , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[6]  David Saunders,et al.  Ten years of art imaging research , 2002, Proc. IEEE.

[7]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[8]  J. Parkkinen,et al.  Characteristic spectra of Munsell colors , 1989 .

[9]  T. W. Anderson,et al.  An Introduction to Multivariate Statistical Analysis , 1959 .

[10]  J. Schmee An Introduction to Multivariate Statistical Analysis , 1986 .

[11]  A. Robertson The CIE 1976 Color-Difference Formulae , 1977 .

[12]  H. Andrews,et al.  Singular value decompositions and digital image processing , 1976 .

[13]  Tamir Hazan,et al.  Sparse image coding using a 3D non-negative tensor factorization , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Yoichi Miyake,et al.  Obtaining and reproduction of accurate color images based on human perception , 1998, Electronic Imaging.

[15]  Arto Kaarna,et al.  Multispectral image compression , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[16]  Jussi Parkkinen,et al.  Optimal sampling of color spectra. , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[18]  E. Oja,et al.  Independent Component Analysis , 2001 .