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Proceedings Paper

Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data
Author(s): Marie Lachaize; Sylvie Le Hégarat-Mascle; Emanuel Aldea; Aude Maitrot; Roger Reynaud
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Paper 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.

Paper Details

Date Published: 14 May 2017
PDF: 8 pages
Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380V (14 May 2017); doi: 10.1117/12.2266961
Show Author Affiliations
Marie Lachaize, SATIE, Univ. Paris-Sud, Univ. Paris-Saclay, CNRS (France)
Veolia Recherche et Innovation (France)
Sylvie Le Hégarat-Mascle, SATIE, Univ. Paris-Sud, Univ. Paris-Saclay, CNRS (France)
Emanuel Aldea, SATIE, Univ. Paris-Sud, Univ. Paris-Saclay, CNRS (France)
Aude Maitrot, Veolia Recherche et Innovation (France)
Roger Reynaud, SATIE, Univ. Paris-Sud, Univ. Paris-Saclay, CNRS (France)


Published in SPIE Proceedings Vol. 10338:
Thirteenth International Conference on Quality Control by Artificial Vision 2017
Hajime Nagahara; Kazunori Umeda; Atsushi Yamashita, Editor(s)

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