Improving Image Classification Performance with Automatically Hierarchical Label Clustering
Zhiqiang Chen, Changde Du, Lijie Huang, Dan Li, Huiguang He · 2018
Image classification is a common and foundational problem in computer vision. In traditional image classification, a category is assigned with single label, which is difficult for networks to learn better features. On the contrary, hierarchical labels can depict the structure of categories better, which helps network to learn more hierarchical features and improve the classification performance. Though many datasets contain images with multi-labels, the labels in these datasets usually lack of hierarchy. To overcome this problem, we propose a new method to improve image classification performance with Automatically Hierarchical Label Clustering (AHLC). Firstly, AHLC calculates the similarity between each pair of original categories by how easily they are misclassified with a pre-trained classifier. Secondly, AHLC obtains hierarchical labels by merging similar categories using hierarchical clustering. Finally, AHLC trains a new classifier with hierarchical labels to improve the original classification performance. We evaluate our method on MNIST and CIFAR-100 datasets and the results demonstrate the superiority of our method. The main contribution of this work is that we can simply improve an existing classification network by AHLC without extra information or heavy architecture redesign.