Comparison of PCA and ICA in color recognition

Hannu Tapani Laamanen; Timo Jaeaeskelaeinen; Jussi P. S. Parkkinen

doi:10.1117/12.403784

11 October 2000 Comparison of PCA and ICA in color recognition

Hannu Tapani Laamanen, Timo Jaeaeskelaeinen, Jussi P. S. Parkkinen

Proceedings Volume 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision; (2000) https://doi.org/10.1117/12.403784
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States

Abstract

It has been shown that a large dataset of color spectra can be represented as a linear combination of a few principal spectra. These principal spectra, which form the basis of a vector-subspace, are usually generated by Principal Component Analysis (PCA), the method widely applied to the analysis of spectral data. The objective of the present study was the comparison of PCA and its extenion Independent Component Analysis (ICA). ICA is a statistical signal processing technique, which tries to express measured signals as a linear combination of unknown source signals. Both methods were applied to a set of 1269 reflectance spectra of the chips in the Munsell Book of Color-Matte Finish Collection and a set of 922 reflectance spectra of the samples in the Pantone Color Formula Guide. Several bases with different number of principal spectra were generated. Each Munsell and Pantone basis was used to reconstruct both the Munsell and the Pantone color spectra. The accuracy of the reconstructability was measured mainly by means of color differences (delta) E_ab^* (CIELAB), but the spectral reconstruction errors were also determined. The dimension of the subspaces leading to a given reconstruction accuracy is discussed in the paper.

Citation Download Citation

Hannu Tapani Laamanen, Timo Jaeaeskelaeinen, and Jussi P. S. Parkkinen "Comparison of PCA and ICA in color recognition", Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, (11 October 2000); https://doi.org/10.1117/12.403784

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available