Context-Aware MIML Instance Annotation

In multi-instance multi-label (MIML) instance
annotation, the goal is to learn an instance classifier while
training on a MIML dataset, which consists of bags of instances
paired with label sets; instance labels are not provided in
the training data. The MIML formulation can be applied in
many domains. For example, in an image domain, bags are
images, instances are feature vectors representing segments in
the images, and the label sets are lists of objects or categories
present in each image. Although many MIML algorithms have
been developed for predicting the label set of a new bag, only
a few have been specifically designed to predict instance labels.
We propose MIML-ECC (ensemble of classifier chains), which
exploits bag-level context through label correlations to improve
instance-level prediction accuracy. The proposed method is
scalable in all dimensions of a problem (bags, instances, classes,
and feature dimension), and has no parameters that require
tuning (which is a problem for prior methods). In experiments
on two image datasets, a bioacoustics dataset, and two artificial
datasets, MIML-ECC achieves higher or comparable accuracy
in comparison to several recent methods and baselines.