Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data

Marie Lachaize; Sylvie Le Hégarat-Mascle; Emanuel Aldea; Aude Maitrot; Roger Reynaud

doi:10.1117/12.2266961

14 May 2017 Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data

Marie Lachaize, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Aude Maitrot, Roger Reynaud

Author Affiliations +

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380V (2017) https://doi.org/10.1117/12.2266961
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

Abstract

Our application deals with waste sorting using an automatic system involving a hyperspectral camera. This latter provides the data for classification of the different kinds of waste allowing the evaluation of mechanical pre-sorting and its refinement. Hyperspectral data are processed using Support Vector Machine (SVM) binary classifiers that we propose to combine in the belief function theory (BFT) framework to take into account not only the performance of each binary classifier, but also its imprecision related for instance to the number of samples during the learning step. Having underlined the interest of BFT framework to deal with sparse classifiers, we study the performance of different combinations of classifiers.

Citation Download Citation

Marie Lachaize, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Aude Maitrot, and Roger Reynaud "Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380V (14 May 2017); https://doi.org/10.1117/12.2266961

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