Transductive attributes for ship category recognition

Ship category recognition is one of the remote sensing applications that requires designing accurate image representation and classification models. Training these models is usually a data hungry process, that requires a lot of labeled data which are usually scarce and expensive. As unlabeled data are more abundant and relatively cheaper, transductive methods exploiting these data are highly preferred. We introduce in this paper a novel representation learning approach based on transductive attributes. The strength of our method resides in its ability to upgrade labeled training sets using attributes shared across categories, and also its ability to further push the benefit of attributes by taking into account not only the labeled data but also the abundant unlabeled ones. When plugging these learned attribute representations into transductive classifiers, we obtain a substantial gain of accuracy compared to several baselines as well as the related work, on the challenging task of ship category recognition, especially when labeled training data are very scarce.