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

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.