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Fusion of evidential CNN classifiers for image classification

Authors :
Tong, Zheng
Xu, Philippe
Denoeux, Thierry
Publication Year :
2021

Abstract

We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extract features from input images and convert them into mass functions on different frames of discernment. A fusion module then aggregates these mass functions using Dempster's rule. An end-to-end learning procedure allows us to fine-tune the overall architecture using a learning set with soft labels, which further improves the classification performance. The effectiveness of this approach is demonstrated experimentally using three benchmark databases.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2108.10233
Document Type :
Working Paper