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.