Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multi-label image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. The reason is 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. We call it two-dimensional active learning because it considers both the sample dimension and the label dimension. Furthermore because the number of training samples is increasing rapidly over time due to active learning, it becomes intractable for the offline learner to retrain a new model on the whole training set. So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance under a set of multi-label constraints. The effectiveness and efficiency of the proposed method are evaluated on two benchmark datasets and a realistic image collection from a real-world image sharing website – Corbis.<\/p>\n","protected":false},"excerpt":{"rendered":"
Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multi-label image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. 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