Evaluation of alternative spectral feature extraction methods of textural images for multivariate modelling

Abstract

Fast and automatic strategies for extraction of characteristic feature spectra from digital images are investigated. We present a study based on images from confocal laser scanning microscopy (CLSM) of mayonnaise. Based on principal component regression (PCR), six different methods are compared with respect to prediction of external measurements describing the sensory texture of samples. The methods considered are: 1, the magnitude spectrum of the Fourier transform; 2, the autocorrelation spectrum; 3, the autocovariance spectrum; 4, the absolute difference spectrum; 5, the singular value spectrum; 6, the angle measure technique. A technique based on cross-validated predictions combined with a two-way ANOVA is suggested to decide eventual differences in prediction ability. © 1998 John Wiley & Sons, Ltd.