Abstract
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.
Original language | English |
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Pages (from-to) | 1059-1073 |
Number of pages | 15 |
Journal | Pattern Recognition |
Volume | 48 |
Issue number | 4 |
DOIs | |
State | Published - 1 Apr 2015 |
Externally published | Yes |
Keywords
- Animal biometrics
- Conservation
- Crowdsourcing
- Gecko
- Hybrid shape contexts
- Individual identification
- Local features
- Photo-identification
- Relevance feedback
- Salamander
- Scale-cascaded alignment
- Skink
- Whale shark