ConvNet and Dempster-Shafer Theory for Object Recognition

  • Zheng Tong
  • Philippe Xu
  • Thierry DenœuxEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11940)


We propose a novel classifier based on convolutional neural network (ConvNet) and Dempster-Shafer theory for object recognition allowing for ambiguous pattern rejection, called the ConvNet-BF classifier. In this classifier, a ConvNet with nonlinear convolutional layers and a global pooling layer extracts high-dimensional features from input data. The features are then imported into a belief function classifier, in which they are converted into mass functions and aggregated by Dempster’s rule. Evidence-theoretic rules are finally used for pattern classification and rejection based on the aggregated mass functions. We propose an end-to-end learning strategy for adjusting the parameters in the ConvNet and the belief function classifier simultaneously and determining the rejection loss for evidence-theoretic rules. Experiments with the CIFAR-10, CIFAR-100, and MNIST datasets show that hybridizing belief function classifiers with ConvNets makes it possible to reduce error rates by rejecting patterns that would otherwise be misclassified.


Pattern recognition Belief function Convolutional neural network Supervised learning Evidence theory 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Université de Technologie de Compiègne, CNRS, UMR 7253 HeudiasycCompiègneFrance

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