  Learning an encoding of feature vectors in terms of an over-complete
dictionary or a information geometric (Fisher vectors) construct is wide-spread
in statistical signal processing and computer vision. In content based
information retrieval using deep-learning classifiers, such encodings are
learnt on the flattened last layer, without adherence to the multi-linear
structure of the underlying feature tensor. We illustrate a variety of feature
encodings incl. sparse dictionary coding and Fisher vectors along with
proposing that a structured tensor factorization scheme enables us to perform
retrieval that can be at par, in terms of average precision, with Fisher vector
encoded image signatures. In short, we illustrate how structural constraints
increase retrieval fidelity.
