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Hierarchical learning with backtracking algorithm based on the Visual Confusion Label Tree for large-scale image classification

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Abstract

In this paper, a hierarchical learning algorithm based on the Bayesian Neural Network classifier with backtracking is proposed to support large-scale image classification, where a Visual Confusion Label Tree is established for constructing a hierarchical structure for large numbers of categories in image datasets and determining the hierarchical learning tasks automatically. Specifically, the Visual Confusion Label Tree is established based on outputs of convolution neural network models. One parent node on the Visual Confusion Label Tree contains a set of sibling coarse-grained categories, and child nodes have several sets of fine-grained categories which are partitions of categories on the parent node. The proposed Hierarchical Bayesian Neural Network with backtracking algorithm can benefit from the hierarchical structure of the Visual Confusion Label Tree. Focusing on those confusion subsets instead of the entire set of categories makes the classification ability of the tree classifier stronger. The backtracking algorithm can utilize the uncertainty information captured from the Bayesian Neural Network to make a second classification to re-correct samples that were classified incorrectly in the previous classification process. Experiments on four large-scale datasets show that our tree classifier obtains a significant improvement over the state-of-the-art tree classifier, which have demonstrated the discriminative hierarchical structure of our Visual Confusion Label Tree and the effectiveness of our Hierarchical Bayesian Neural Network with backtracking algorithm.

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Authors and Affiliations

  1. National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Hunan, 410073, Changsha, China

    Yuntao Liu, Yong Dou, Ruochun Jin, Rongchun Li & Peng Qiao

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  1. Yuntao Liu
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  2. Yong Dou
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  3. Ruochun Jin
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  4. Rongchun Li
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  5. Peng Qiao
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Correspondence to Yuntao Liu.

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All authors declare that they have no conflict of interest.

Funding

This study was funded by the Ministry of Science and Technology of the People’s Republic of China (Grant Number 2018YFB1003400), National Natural Science Foundation of China (Grant Number 61802419) and National Natural Science Foundation of China (Grant Number 61902415).

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Liu, Y., Dou, Y., Jin, R. et al. Hierarchical learning with backtracking algorithm based on the Visual Confusion Label Tree for large-scale image classification. Vis Comput 38, 897–917 (2022). https://doi.org/10.1007/s00371-021-02058-w

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