Visual Microfossil Identificationvia Deep Metric Learning

• URL: http://arxiv.org/abs/2112.09490v1
• Date: Fri, 17 Dec 2021 13:00:37 GMT
• Title: Visual Microfossil Identificationvia Deep Metric Learning
• Authors: Tayfun Karaderi, Tilo Burghardt, Allison Y. Hsiang, Jacob Ramaer, Daniela N. Schmidt
• Abstract summary: We apply metric learning to classifying planktic foraminifer shells on microscopic images. We produce the first scientific visualisation of the phenotypic planktic foraminifer space. We show that metric learning out-performs all published CNN-based state-of-the-art benchmarks in this domain.
• Score: 1.3199511198128897
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We apply deep metric learning for the first time to the prob-lem of classifying planktic foraminifer shells on microscopic images. This species recognition task is an important information source and scientific pillar for reconstructing past climates. All foraminifer CNN recognition pipelines in the literature produce black-box classifiers that lack visualisation options for human experts and cannot be applied to open set problems. Here, we benchmark metric learning against these pipelines, produce the first scientific visualisation of the phenotypic planktic foraminifer morphology space, and demonstrate that metric learning can be used to cluster species unseen during training. We show that metric learning out-performs all published CNN-based state-of-the-art benchmarks in this domain. We evaluate our approach on the 34,640 expert-annotated images of the Endless Forams public library of 35 modern planktic foraminifera species. Our results on this data show leading 92% accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data, and 66.5% accuracy (at 0.70 F1-score) when clustering species never encountered in training. We conclude that metric learning is highly effective for this domain and serves as an important tool towards expert-in-the-loop automation of microfossil identification. Key code, network weights, and data splits are published with this paper for full reproducibility.

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