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ConvNet and Dempster-Shafer Theory for Object Recognition

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Scalable Uncertainty Management (SUM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11940))

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Abstract

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.

This research was carried out in the framework of the Labex MS2T, which was funded by the French Government, through the program “Investments for the future” managed by the National Agency for Research (Reference ANR- 11-IDEX-0004-02). It was also supported by a scholarship from the China Scholarship Council.

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Authors and Affiliations

  1. Université de Technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, Compiègne, France

    Zheng Tong, Philippe Xu & Thierry Denœux

Authors
  1. Zheng Tong
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  2. Philippe Xu
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  3. Thierry Denœux
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Correspondence to Thierry Denœux .

Editor information

Editors and Affiliations

  1. Institut Supérieur de Gestion de Tunis, Bouchoucha, Tunisia

    Nahla Ben Amor

  2. University of Technology of Compiègne, Compiègne, France

    Benjamin Quost

  3. University of Luxembourg, Esch-Sur-Alzette, Luxembourg

    Martin Theobald

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Tong, Z., Xu, P., Denœux, T. (2019). ConvNet and Dempster-Shafer Theory for Object Recognition. In: Ben Amor, N., Quost, B., Theobald, M. (eds) Scalable Uncertainty Management. SUM 2019. Lecture Notes in Computer Science(), vol 11940. Springer, Cham. https://doi.org/10.1007/978-3-030-35514-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-35514-2_27

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