HotSpotter-Patterned species instance recognition

Jonathan P. Crall, Charles V. Stewart, Tanya Y. Berger-Wolf, Daniel Ian Rubenstein, Siva R. Sundaresan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

121 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Number of pages8
StatePublished - 2013
Event2013 IEEE Workshop on Applications of Computer Vision, WACV 2013 - Clearwater Beach, FL, United States
Duration: Jan 15 2013Jan 17 2013

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986


Other2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Country/TerritoryUnited States
CityClearwater Beach, FL

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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