  The MIT Sloop system indexes and retrieves photographs from databases of
non-stationary animal population distributions. To do this, it adaptively
represents and matches generic visual feature representations using sparse
relevance feedback from experts and crowds. Here, we describe the Sloop system
and its application, then compare its approach to a standard deep learning
formulation. We then show that priming with amplitude and deformation features
requires very shallow networks to produce superior recognition results. Results
suggest that relevance feedback, which enables Sloop's high-recall performance
may also be essential for deep learning approaches to individual identification
to deliver comparable results.
