A novel deep ordinal classification approach for aesthetic quality control classification
Riccardo Rosati 
Luca Romeo 
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez 
César Hervás-Martínez Neural Computing and Applications
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abstract
Nowadays, decision support systems (DSSs) are widely used in several application domains, from industrial to healthcare and medicine fields. Concerning the industrial scenario, we propose a DSS oriented to the aesthetic quality control (AQC) task, which has quickly established itself as one of the most crucial challenges of Industry 4.0. Taking into account the increasing amount of data in this domain, the application of machine learning (ML) and deep learning (DL) techniques offers great opportunities to automatize the overall AQC process. State-of-the-art is mainly oriented to approach this problem with a nominal DL classification method which does not exploit the ordinal structure of the AQC task, thus not penalizing the error among distant AQC classes (which is a relevant aspect for the real use case). The paper introduces a DL ordinal methodology for the AQC classification. Differently from other deep ordinal methods, we combined the standard categorical cross-entropy with the cumulative link model and we imposed the ordinal constraint via the thresholds and slope parameters. Experimental results were performed for solving an AQC task on a novel image dataset originated from a specific company's demand (i.e., aesthetic assessment of wooden stocks). We demonstrated how the proposed methodology is able to reduce misclassification errors (up to 0.937 quadratic weight kappa loss) among distant classes while overcoming other state-of-the-art deep ordinal models and reducing the bias factor related to the item geometry. The proposed DL approach was integrated as the main core of a DSS supported by Internet of Things (IoT) architecture that can support the human operator by reducing up to 90
Keywords
BibTex Citation
@article{Rosati2022novel,
author = {Rosati, Riccardo and Romeo, Luca and Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar and Frontoni, Emanuele},
journal = {Neural Computing and Applications},
doi = {10.1007/s00521-022-07050-6},
year = {2022},
pages = {11625--11639},
title = {A novel deep ordinal classification approach for aesthetic quality control classification},
url = {https://dx.doi.org/10.1007/s00521-022-07050-6},
howpublished = {https://dx.doi.org/10.1007/s00521-022-07050-6},
volume = {34},
}
BibTex Unicode Citation
@article{Rosati2022novel,
author = {Rosati, Riccardo and Romeo, Luca and Vargas-Yun, Víctor Manuel and Gutiérrez, Pedro Antonio and Hervás-Martínez, César and Frontoni, Emanuele},
journal = {Neural Computing and Applications},
doi = {10.1007/s00521-022-07050-6},
year = {2022},
pages = {11625--11639},
title = {A novel deep ordinal classification approach for aesthetic quality control classification},
url = {https://dx.doi.org/10.1007/s00521-022-07050-6},
howpublished = {https://dx.doi.org/10.1007/s00521-022-07050-6},
volume = {34},
}
APA Citation
Rosati, R., Romeo, L., Vargas-Yun, V. M., Gutiérrez, P. A., Hervás-Martínez, C., & Frontoni, E. (2022). A novel deep ordinal classification approach for aesthetic quality control classification. Neural Computing and Applications, 34, 11625–11639. https://doi.org/10.1007/s00521-022-07050-6
RIS Citation
TY - JOUR
AU - Rosati, Riccardo
AU - Romeo, Luca
AU - Vargas-Yun, Víctor Manuel
AU - Gutiérrez, Pedro Antonio
AU - Hervás-Martínez, César
AU - Frontoni, Emanuele
DA - 2022///
PY - 2022
DO - 10.1007/s00521-022-07050-6
ID - temp_id_791821816255
SP - 11625-11639
T2 - Neural Computing and Applications
TI - A novel deep ordinal classification approach for aesthetic quality con
trol classification
UR - https://dx.doi.org/10.1007/s00521-022-07050-6
VL - 34
ER -
CV Citation
R. Rosati (CA), L. Romeo, V.M. Vargas-Yun, P.A. Gutiérrez, C. Hervás-Martínez, E. Frontoni (3/6). "A novel deep ordinal classification approach for aesthetic quality control classification". Neural Computing and Applications, Vol. 34, pp. 11625–11639, 2022. (Q2, IF: 6.0).