Premium
Evaluation of alternative spectral feature extraction methods of textural images for multivariate modelling
Author(s) -
Indahl Ulf G.,
Naes Tormod
Publication year - 1998
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/(sici)1099-128x(199807/08)12:4<261::aid-cem513>3.0.co;2-z
Subject(s) - autocovariance , principal component analysis , pattern recognition (psychology) , artificial intelligence , mathematics , multivariate statistics , autocorrelation , feature extraction , measure (data warehouse) , feature (linguistics) , statistics , biological system , computer science , fourier transform , data mining , mathematical analysis , linguistics , philosophy , biology
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.