基于数据增强多视图深度学习集成的化石图像识别
Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
AI总结：
针对带标注化石图像数量质量有限的问题，提出融合原始、灰度、骨架三视图的深度学习集成框架OGS，在蜓类化石识别任务中性能优于基线，且与人类专家识别一致性更高。
摘要：
化石物种识别对进化研究至关重要。深度学习的最新进展在化石图像识别领域展现出良好前景。然而，受化石保存、条件采样以及领域专家标注成本高昂且标注标准不一致等因素影响，带标注化石图像的数量和质量往往有限，这给训练基于深度学习的图像分类模型带来了巨大挑战。为解决这些难题，我们遵循群体智慧理念，提出一种多视图集成框架：该框架采集每幅化石图像的原始（Original, O）、灰度（Gray, G）和骨架（Skeleton, S）三种反映不同特征的视图，用于训练多个基模型，随后通过软投票得出最终决策。在包含2400幅图像的最大蜓类化石数据集上开展的实验表明，所提出的OGS方法始终优于各基线方法（针对每个视图使用单个模型），且与OOO方法（针对三个相同的原始视图使用三个基模型）相比，性能更优或相当。此外，随着训练数据量减少，所提框架的性能增益更为显著。在评估与人类专家的识别一致性方面，OGS与数据集原始标签以及两位人类专家的重新识别结果的一致性最高。验证性能为不同专家和属之间的一致性提供了量化估计。我们得出结论，在蜓类化石识别案例研究中，所提框架能达到当前最优性能。该框架是为通用化石识别设计的，有望在未来研究中应用于其他化石数据集。源代码已公开于https://github.com/houchengbin/Fossil-Image-Identification，以助力化石图像识别领域的后续研究。
英文摘要：
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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