  The problem of vehicle licence plate re-identification is generally
considered as a one-shot image retrieval problem. The objective of this task is
to learn a feature representation (called a "signature") for licence plates.
Incoming licence plate images are converted to signatures and matched to a
previously collected template database through a distance measure. Then, the
input image is recognized as the template whose signature is "nearest" to the
input signature. The template database is restricted to contain only a single
signature per unique licence plate for our problem.
  We measure the performance of deep convolutional net-based features adapted
from face recognition on this task. In addition, we also test a hybrid approach
combining the Fisher vector with a neural network-based embedding called "f2nn"
trained with the Triplet loss function. We find that the hybrid approach
performs comparably while providing computational benefits. The signature
generated by the hybrid approach also shows higher generalizability to datasets
more dissimilar to the training corpus.
