HEp-2 cells Classification via clustered multi-task learning

This paper proposes a clustered multi-task learning-based method for automated HEp-2 cells Classification. First, the visual feature is extracted for individual sample to represent its appearance characteristics. Then, the models of multiple HEp-2 cell category are jointly trained in the framework of clustered multi-task learning. The extensive experiments on the HEp 2, cell dataset released by the HEp-2 Cells Classification contest, held at the 2012 International Conference on Patter Recognition, show that the proposed method can discover and share the latent relatedness among multiple tasks and consequently augment the performance. The quantitative comparison against the state-of-the-art methods demonstrates the superiority of the proposed method.