  Measuring visual similarity between two or more instances within a data
distribution is a fundamental task in image retrieval. Theoretically,
non-metric distances are able to generate a more complex and accurate
similarity model than metric distances, provided that the non-linear data
distribution is precisely captured by the system. In this work, we explore
neural networks models for learning a non-metric similarity function for
instance search. We argue that non-metric similarity functions based on neural
networks can build a better model of human visual perception than standard
metric distances. As our proposed similarity function is differentiable, we
explore a real end-to-end trainable approach for image retrieval, i.e. we learn
the weights from the input image pixels to the final similarity score.
Experimental evaluation shows that non-metric similarity networks are able to
learn visual similarities between images and improve performance on top of
state-of-the-art image representations, boosting results in standard image
retrieval datasets with respect standard metric distances.
