Experimental Study of Unsupervised Feature Learning for HEp-2 Cell Images Clustering

Abstract

Automatic identification of HEp-2 cell images has received an increasing research attention. Feature representations play a critical role in achieving good identification performance. Much recent work has focused on supervised feature learning. Typical methods consist of BoW model (based on hand-crafted features) and deep learning model (learning hierarchical features). However, these labels used in supervised feature learning are very labour-intensive and time-consuming. They are commonly manually annotated by specialists and very expensive to obtain. In this paper, we follow this fact and focus on unsupervised feature learning. We have verified and compared the features of these two typical models by clustering. Experimental results show the BoW model generally perform better than deep learning models. Also, we illustrate BoW model and deep learning models have complementarity properties.