Automatic Unseen Class Discovery Algorithm Based on Clustering Analysis

Bin Li, Yiwei Li, Yuxuan Sun, Zongyong Cui, Zongjie Cao, Jianyu Yang

Abstract: Although deep learning has achieved great success in automatic target recognition, the model needs a large number of labeled samples for training. In real life, it is a time-consuming and laborious work to label unlabeled samples, so how to find unknown classes from a large number of unlabeled samples has aroused widespread concern. In this paper, we study how to discover unseen class from an unlabeled image set under the assumption that there are samples related to the unseen class but of different classes as prior knowledge. The Automatic Unseen Class Discovery (AUCD) algorithm is proposed in this paper, which mainly solves the problem of unseen class discovery from two aspects, one is how to actively form clusters according to their classes for unknown class samples, and the other is how to obtain the number of formed clusters. Several experiments based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the effectiveness of the proposed approach in the field of unseen class discovery.