Two-Dimensional Active Learning for image classification

In this paper, we propose a two-dimensional active learning scheme and show its application in image classification. Traditional active learning methods select samples only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multi-label classification. In multi-label classification, we argue that, for each selected sample, only a part of more effective labels are necessary to be annotated while others can be inferred by exploring the correlations among the labels. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multi-label Bayesian classification error bound. This new active learning strategy not only considers the sample dimension but also the label dimension, and we call it Two-Dimensional Active Learning (2DAL). We also show that the traditional active learning formulation is a special case of 2DAL when there is only one label. Extensive experiments conducted on two real-world applications show that the 2DAL significantly outperforms the best existing approaches which did not take label correlation into account.