Large-scale multi-task image labeling with adaptive relevance discovery and feature hashing

It remains challenging to train an effective classifier for the new image classification tasks provided with only a few or even no labeled samples. Although multi-task learning approaches have been introduced into this field to exploit available label information to boost classification accuracy, these approaches discover intrinsic task relationships only at task level, which will lead to limited useful labels being exploited and shared. Motivated by clustered multi-task learning, this paper proposes a robust multi-task feature hashing learning algorithm for image classification. Specifically, the original input samples are first projected into a low-dimensional hash feature subspace, upon which not only the inherent relatedness but also the fine-grained clustering among samples can be revealed well. Then, the task relationships are captured by interacting at task level as well as at feature level, and finally the auxiliary labels can be shared across different tasks. We conduct extensive experiments on three large-scale multi-label image classification datasets, and results demonstrate the superiorities of the proposed formulation in comparison with several state-of-the-arts.