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Sloop: A pattern retrieval engine for individual animal identification
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DOI：10.1016/j.patcog.2014.07.017.png)
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Abstract
En 中文
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. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Photo-identification
Animal biometrics
Individual identification
Relevance feedback
Crowdsourcing
Conservation
Scale-cascaded alignment
Local features
Hybrid shape contexts
Gecko
Skink
Whale shark
Salamander
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