Sloop: A pattern retrieval engine for individual animal identification

•First individual animal identification system based on relevance feedback.•State-of-the-art results for five species with operational use in two of them.•A framework to create scalable and extensible systems with multiple methods.•An iterative strategy for integration of vision, biology, crowds and citizen science.

Identifying individuals in photographs of animals collected over time is a non-invasive approach for ecological monitoring and conservation. This paper describes the design and use of Sloop, the first image retrieval system for individual animal identification incorporating crowd-sourced relevance feedback. Sloop׳s iterative retrieval strategy using hierarchical and aggregated matching and relevance feedback consistently improves deformation and correspondence-based approaches for individual identification across several species. Its crowdsourcing strategy is successful in utilizing relevance feedback on a large scale. Sloop is in operational use. The user experience and results are presented here to facilitate the creation of a community-based individual identification system for conservation planning.