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The MIT Sloop system indexes and retrieves photographs from databases ofnon-stationary animal population distributions. To do this, it adaptivelyrepresents and matches generic visual feature representations using sparserelevance feedback from experts and crowds. Here, we describe the Sloop systemand its application, then compare its approach to a standard deep learningformulation. We then show that priming with amplitude and deformation featuresrequires very shallow networks to produce superior recognition results. Resultssuggest that relevance feedback, which enables Sloop's high-recall performancemay also be essential for deep learning approaches to individual identificationto deliver comparable results.

The Sloop System for Individual Animal Identification with Deep Learning