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This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re\u2010identification can be done by analysing the permanent fur, feather, or skin patterns that are unique to each individual. In this paper, the authors study pattern feature aggregation based re\u2010identification and consider two ways of improving accuracy: (1) aggregating pattern image features over multiple images and (2) combining the pattern appearance similarity obtained by feature aggregation and geometric pattern similarity. Aggregation over multiple database images of the same individual allows to obtain more comprehensive and robust descriptors while reducing the computation time. On the other hand, combining the two similarity measures allows to efficiently utilise both the local and global pattern features, providing a general re\u2010identification approach that can be applied to a wide variety of different pattern types. In the experimental part of the work, the authors demonstrate that the proposed method achieves promising re\u2010identification accuracies for Saimaa ringed seals and whale sharks without species\u2010specific training or fine\u2010tuning.<\/jats:p>","DOI":"10.1049\/cvi2.12337","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T12:25:10Z","timestamp":1736339110000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Re\u2010identification of patterned animals by multi\u2010image feature aggregation and geometric similarity"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5045-5041","authenticated-orcid":false,"given":"Ekaterina","family":"Nepovinnykh","sequence":"first","affiliation":[{"name":"Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta\u2010Lahti University of Technology LUT  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