F. Ros et al., COMBINING GLOBAL AND INDIVIDUAL IMAGE FEATURES TO CHARACTERIZE GRANULAR PRODUCT POPULATIONS, Journal of chemometrics, 11(6), 1997, pp. 483-500
The characterization of granular product populations using image analy
sis is a difficult problem because it often requires the extraction an
d combination of many different features. We propose to study in a gen
eral way these problems of granular product classification, considerin
g the image analysis phase, the processing of the information extracte
d and the decision making, In this paper we focus rather on the decisi
on system development. It is based on a hierarchical approach to the p
roblem, in eluding a generalist system whose outputs are ambiguous (an
approximative solution), connected to specialist systems trained to g
ive non-ambiguous solutions, The inputs of the generalist system are t
he components of a vector containing the most important information fo
r discriminating all the decision classes, while the inputs of the spe
cialist systems are those which best distinguish a given class from an
other. This strategy enables us to overcome the multiclass aspect of t
he problem. It is independent of the choice of the techniques to selec
t the pertinent information and to take the decision, This method is a
pplied in the framework of a meal classification where three types of
classifier (discriminant analysis, k nearest neighbours and multilayer
neural networks) are compared. (C) 1997 John Wiley & Sons, Ltd.