Semi-automatic labeling with active learning for multi-label image classification

For multi-label image classification, we use active learning to select example-label pairs to acquire labels from experts. The core of active learning is to select the most informative examples to request their labels. Most previous studies in active learning for multi-label classification have two shortcomings. One is that they didn’t pay enough attention on label correlations. The other shortcoming is that existing example-label selection methods predict all the rest labels of the selected example-label pair. This leads to a bad performance for classification when the number of the labels is large. In this paper, we propose a semi-automatic labeling multi-label active learning (SLMAL) algorithm. Firstly, SLMAL integrates uncertainty and label informativeness to select example-label pairs to request labels. Then we choose the most uncertain example-label pair and predict its partial labels using its nearest neighbor. Our empirical results demonstrate that our proposed method SLMAL outperforms the state-of-the-art active learning methods for multi-label classification. It significantly reduces the labeling workloads and improves the performance of a classifier built.