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Combining global and individual image features to characterize granular product populations
Author(s) -
Ros F.,
Guillaume S.,
Rabatel G.,
Sevila F.,
Bertrand D.
Publication year - 1997
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(199711/12)11:6<483::aid-cem490>3.0.co;2-8
Subject(s) - computer science , artificial intelligence , machine learning , classifier (uml) , linear discriminant analysis , pattern recognition (psychology) , image (mathematics) , data mining
The characterization of granular product populations using image analysis is a difficult problem because it often requires the extraction and combination of many different features. We propose to study in a general way these problems of granular product classification, considering the image analysis phase, the processing of the information extracted and the decision making. In this paper we focus rather on the decision system development. It is based on a hierarchical approach to the problem, including a generalist system whose outputs are ambiguous (an approximative solution), connected to specialist systems trained to give non‐ambiguous solutions. The inputs of the generalist system are the components of a vector containing the most important information for discriminating all the decision classes, while the inputs of the specialist systems are those which best distinguish a given class from another. This strategy enables us to overcome the multiclass aspect of the problem. It is independent of the choice of the techniques to select the pertinent information and to take the decision. This method is applied in the framework of a meal classification where three types of classifier (discriminant analysis, k nearest neighbours and multilayer neural networks) are compared. © 1997 John Wiley & Sons, Ltd.