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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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Organisationer och upphovspersoner

Lappeenrannan–Lahden teknillinen yliopisto LUT

Nepovinnykh Ekaterina

Kälviäinen Heikki Orcid -palvelun logo

Eerola Tuomas Orcid -palvelun logo

Immonen Veikka

Publikationstyp

Publikationsform

Artikel

Moderpublikationens typ

Tidning

Artikelstyp

En originalartikel

Målgrupp

Vetenskaplig

Kollegialt utvärderad

Kollegialt utvärderad

UKM:s publikationstyp

A1 Originalartikel i en vetenskaplig tidskrift

Publikationskanalens uppgifter

Publikationsforum

57617

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