Name
Abstract | ||
|---|---|---|
Hierarchical classification has become one of the most popular research topics because the scale of data has increased exponentially. Top-down hierarchical classification is an effective classification method using the hierarchical class structure as important side information. However, inter-level error propagation is a crucial problem in the top-down strategy. In this paper, we propose a hierarchical classifier based on deep branch convolutional neural networks, which achieves hierarchical classification based on coarse- to fine-grained knowledge transfer. Specifically, we use a deep convolutional neural network to extract image rough and detailed features from shallow and deep networks. We then embed the branch network at different depths of the convolutional network for hierarchical classification. We splice features from the previous branch network to the current branch. It alleviates inter-level error propagation by knowledge transfer from coarse- to fine-grained. Experimental results on four datasets show that the proposed method outperforms nine popular classifiers for hierarchical classification. Especially on the CIFAR10 dataset, our method is about 5% better than the second-best method. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ijar.2022.07.002 | International Journal of Approximate Reasoning |
Keywords | DocType | Volume |
Hierarchical classification,Deep convolutional network,Branch network,Class hierarchical structure | Journal | 149 |
ISSN | Citations | PageRank |
0888-613X | 0 | 0.34 |
References | Authors | |
0 | 3 | |