Fusion of evidential CNN classifiers for image classification

• URL: http://arxiv.org/abs/2108.10233v1
• Date: Mon, 23 Aug 2021 15:12:26 GMT
• Title: Fusion of evidential CNN classifiers for image classification
• Authors: Zheng Tong and Philippe Xu and Thierry Denoeux
• Abstract summary: 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.
• Score: 6.230751621285322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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