EDEN: Deep Feature Distribution Pooling for Saimaa Ringed Seals Pattern Matching Ilia Chelak12, Ekaterina Nepovinnykh2, Tuomas Eerola2, Heikki K¨alvi¨ainen2, and Igor Belykh1 1 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation
[email protected],
[email protected], 2 Lappeenranta-Lahti University of Technology LUT, School of Engineering Science, Department of Computational Engineering, Computer Vision and Pattern Recognition Laboratory, P.O.Box 20, 53850 Lappeenranta, Finland
[email protected] Abstract. In this paper, pelage pattern matching is considered to solve the individual re-identification of the Saimaa ringed seals. Animal re- identification together with the access to large amount of image material through camera traps and crowd-sourcing provide novel possibilities for animal monitoring and conservation. We propose a novel feature pool- ing approach that allow aggregating the local pattern features to get a fixed size embedding vector that incorporate global features by tak- ing into account the spatial distribution of features. This is obtained by eigen decomposition of covariances computed for probability mass func- tions representing feature maps. Embedding vectors can then be used to find the best match in the database of known individuals allowing ani- mal re-identification. The results show that the proposed pooling method outperforms the existing methods on the challenging Saimaa ringed seal image data. Keywords: pattern matching, global pooling, animal biometrics, Saimaa ringed seals 1 Introduction Automatic camera traps (game cameras) and crowd-sourcing provide tools for collecting large volumes of image material to monitor and to study wildlife an- imals. This has made it possible for researchers to obtain versatile and novel arXiv:2105.13979v2 [cs.CV] 24 Aug 2021 knowledge about animal populations such as population size, moving patterns, and social behaviour.