Individual Common Dolphin Identification Via Metric Embedding Learning

By Soren Bouma, Matthew D. M. Pawley, Krista Hupman, Andrew Gilman. 2018

40 citations recorded by the source corpus.

Photo-identification (photo-id) of dolphin individuals is a commonly used technique in ecological sciences to monitor state and health of individuals, as well as to study the social structure and distribution of a population. Traditional photo-id involves a laborious manual process of matching each dolphin fin photograph captured in the field to a catalogue of known individuals. We examine this problem in the context of open-set recognition and utilise a triplet loss function to learn a compact representation of fin images in a Euclidean embedding, where the Euclidean distance metric represents fin similarity. We show that this compact representation can be successfully learnt from a fairly small (in deep learning context) training set and still generalise well to out-of-sample identities (completely new dolphin individuals), with top-1 and top-5 test set (37 individuals) accuracy of 90.5 ± 2 and 93.6 ± 1 percent. In the presence of 1200 distractors, top-1 accuracy dropped by 12%; however, top-5 accuracy saw only a 2.8% drop.

Subfields: Computer Vision and Pattern Recognition, Ecology, Oceanography, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Ecology

Citations in the corpus

NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation (2020, methodology): “As such it is believed that an automated photo-id process is achievable using computer vision (19), with work already beginning in this area (3, 5, 6, 18).”