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HotSpotter-Patterned species instance recognition

Author(s): Crall, JP; Stewart, CV; Berger-Wolf, TY; Rubenstein, Daniel I.; Sundaresan, SR

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Abstract: We present HotSpotter, a fast, accurate algorithm for identifying individual animals against a labeled database. It is not species specific and has been applied to Grevy's and plains zebras, giraffes, leopards, and lionfish. We describe two approaches, both based on extracting and matching keypoints or 'hotspots'. The first tests each new query image sequentially against each database image, generating a score for each database image in isolation, and ranking the results. The second, building on recent techniques for instance recognition, matches the query image against the database using a fast nearest neighbor search. It uses a competitive scoring mechanism derived from the Local Naive Bayes Nearest Neighbor algorithm recently proposed for category recognition. We demonstrate results on databases of more than 1000 images, producing more accurate matches than published methods and matching each query image in just a few seconds. © 2013 IEEE.
Publication Date: 4-Apr-2013
Citation: Crall, JP, Stewart, CV, Berger-Wolf, TY, Rubenstein, DI, Sundaresan, SR. (2013). HotSpotter-Patterned species instance recognition. Proceedings of IEEE Workshop on Applications of Computer Vision, 230 - 237. doi:10.1109/WACV.2013.6475023
DOI: doi:10.1109/WACV.2013.6475023
ISSN: 2158-3978
EISSN: 2158-3986
Pages: 230 - 237
Type of Material: Conference Paper
Journal/Proceeding Title: Proceedings of IEEE Workshop on Applications of Computer Vision
Version: Author's manuscript

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