  In multiple instance multiple label learning, each sample, a bag, consists of
multiple instances. To alleviate labeling complexity, each sample is associated
with a set of bag-level labels leaving instances within the bag unlabeled. This
setting is more convenient and natural for representing complicated objects,
which have multiple semantic meanings. Compared to single instance labeling,
this approach allows for labeling larger datasets at an equivalent labeling
cost. However, for sufficiently large datasets, labeling all bags may become
prohibitively costly. Active learning uses an iterative labeling and retraining
approach aiming to provide reasonable classification performance using a small
number of labeled samples. To our knowledge, only a few works in the area of
active learning in the MIML setting are available. These approaches can provide
practical solutions to reduce labeling cost but their efficacy remains unclear.
In this paper, we propose a novel bag-class pair based approach for active
learning in the MIML setting. Due to the partial availability of bag-level
labels, we focus on the incomplete-label MIML setting for the proposed active
learning approach. Our approach is based on a discriminative graphical model
with efficient and exact inference. For the query process, we adapt active
learning criteria to the novel bag-class pair selection strategy. Additionally,
we introduce an online stochastic gradient descent algorithm to provide an
efficient model update after each query. Numerical experiments on benchmark
datasets illustrate the robustness of the proposed approach.
