Fossil image identification using deep learning ensembles of data augmented multiviews

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 labelled 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), Grey (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. Notably, the result, which shows more performance gains as train set size decreases or over a smaller imbalance fossil dataset, suggests the potential application to identify rare fossil images. The proposed framework also demonstrates its potential for assessing and resolving inconsistencies in fossil identification.

Enhancing computer image recognition with improved image algorithms

“…Regression methods, which originated from the field of statistics, involve predicting a continuous output variable based on input features. In the context of image recognition, regression techniques can be used to learn the mapping from image features to the categories they represent 6 . A novel texture descriptor, MT-ULTP, for cell phenotype classification in fluorescence microscope images, outperforming existing texture descriptors and emphasizing the importance of considering uniform textural patterns in image analysis 7 .Nasim Kayhan 8 proposes a new approach for content-based image retrieval using a weighted combination of color and texture features, outperforming state-of-the-art methods in terms of precision and recall rate, based on experiments conducted on the Corel 1K and Corel 10K datasets.…”