Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews

• URL: http://arxiv.org/abs/2302.08062v3
• Date: Fri, 2 Feb 2024 02:04:32 GMT
• Title: Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
• Abstract summary: We propose a multiview ensemble framework, which collects Original (O), Gray (G), and Skeleton (S) views of each fossil image. Experiments on the largest fusulinid dataset with 2400 images show that the proposed OGS consistently outperforms baselines. OGS receives the highest agreement with the original labels of dataset and with the re-identifications of two human experts.
• Score: 8.877286028859602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labeled fossil images are often limited due to fossil preservation, conditioned sampling, and expensive and inconsistent label annotation by domain experts, which pose great challenges to training deep learning based image classification models. To address these challenges, we follow the idea of the wisdom of crowds and propose a multiview ensemble framework, which collects Original (O), Gray (G), and Skeleton (S) views of each fossil image reflecting its different characteristics to train multiple base models, and then makes the final decision via soft voting. Experiments on the largest fusulinid dataset with 2400 images show that the proposed OGS consistently outperforms baselines (using a single model for each view), and obtains superior or comparable performance compared to OOO (using three base models for three the same Original views). Besides, as the training data decreases, the proposed framework achieves more gains. While considering the identification consistency estimation with respect to human experts, OGS receives the highest agreement with the original labels of dataset and with the re-identifications of two human experts. The validation performance provides a quantitative estimation of consistency across different experts and genera. We conclude that the proposed framework can present state-of-the-art performance in the fusulinid fossil identification case study. This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work. The source code is publicly available at https://github.com/houchengbin/Fossil-Image-Identification to benefit future research in fossil image identification.

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