Re-identification of patterned animals by multi-image feature aggregation and geometric similarity
Publiceringsår
2025
Upphovspersoner
Nepovinnykh Ekaterina; Immonen Veikka; Eerola Tuomas; Stewart Charles V.; Kälviäinen Heikki
Abstrakt
Image-based re-identification of animal individuals allows gathering of information such as population size and migration patterns of the animals over time. This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re-identification 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-identification 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-identification 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-identification accuracies for Saimaa ringed seals and whale sharks without species-specific training or fine-tuning.
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Publikationstyp
Publikationsform
Artikel
Moderpublikationens typ
Tidning
Artikelstyp
En originalartikel
Målgrupp
VetenskapligKollegialt utvärderad
Kollegialt utvärderadUKM:s publikationstyp
A1 Originalartikel i en vetenskaplig tidskriftPublikationskanalens uppgifter
Journal
ISSN
Publikationsforum
Publikationsforumsnivå
1
Öppen tillgång
Öppen tillgänglighet i förläggarens tjänst
Ja
Öppen tillgång till publikationskanalen
Helt öppen publikationskanal
Parallellsparad
Nej
Övriga uppgifter
Vetenskapsområden
Data- och informationsvetenskap
Nyckelord
[object Object],[object Object],[object Object],[object Object]
Förlagets internationalitet
Internationell
Internationell sampublikation
Ja
Sampublikation med ett företag
Nej
DOI
10.1049/cvi2.12337
Publikationen ingår i undervisnings- och kulturministeriets datainsamling
Ja