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Communication Dans Un Congrès Année : 2021

Fusion of evidential CNN classifiers for image classification

Résumé

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.
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Dates et versions

hal-03511144 , version 1 (04-01-2022)

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Zheng Tong, Philippe Xu, Thierry Denœux. Fusion of evidential CNN classifiers for image classification. 6th International Conference on Belief Functions (BELIEF 2021), Oct 2021, Shanghai, China. pp.168-176, ⟨10.1007/978-3-030-88601-1_17⟩. ⟨hal-03511144⟩
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