Individual Common Dolphin Identification Via Metric Embedding Learning
Soren Bouma, Matthew D. M. Pawley, Krista Hupman, Andrew Gilman . 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) 2019 – 2 citations
[Paper]
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\pm2) and (93.6\pm1) percent. In the presence of 1200 distractors, top-1 accuracy dropped by (12%); however, top-5 accuracy saw only a (2.8%) drop