Group-Wise Learning for Aurora Image Classification With Multiple Representations.

Abstract

In conventional aurora image classification methods, it is general to employ only one single feature representation to capture the morphological characteristics of aurora images, which is difficult to describe the complicated morphologies of different aurora categories. Although several studies have proposed to use multiple feature representations, the inherent correlation among these representations are usually neglected. To address this problem, we propose a group-wise learning (GWL) method for the automatic aurora image classification using multiple representations. Specifically, we first extract the multiple feature representations for aurora images, and then construct a graph in each of multiple feature spaces. To model the correlation among different representations, we partition multiple graphs into several groups via a clustering algorithm. We further propose a GWL model to automatically estimate class labels for aurora images and optimal weights for the multiple representations in a data-driven manner. Finally, we develop a label fusion approach to make a final classification decision for new testing samples. The proposed GWL method focuses on the diverse properties of multiple feature representations, by clustering the correlated representations into the same group. We evaluate our method on an aurora image data set that contains 12 682 aurora images from 19 days. The experimental results demonstrate that the proposed GWL method achieves approximately 6% improvement in terms of classification accuracy, compared to the methods using a single feature representation.