  Recognition of objects with subtle differences has been used in many
practical applications, such as car model recognition and maritime vessel
identification. For discrimination of the objects in fine-grained detail, we
focus on deep embedding learning by using a multi-task learning framework, in
which the hierarchical labels (coarse and fine labels) of the samples are
utilized both for classification and a quadruplet-based loss function. In order
to improve the recognition strength of the learned features, we present a novel
feature selection method specifically designed for four training samples of a
quadruplet. By experiments, it is observed that the selection of very hard
negative samples with relatively easy positive ones from the same coarse and
fine classes significantly increases some performance metrics in a fine-grained
dataset when compared to selecting the quadruplet samples randomly. The feature
embedding learned by the proposed method achieves favorable performance against
its state-of-the-art counterparts.
