Graptolite image classification based on feature transfer and mixup data enhancement

Xiaoxiao Xu; Chaofeng Li; Honglin Liu

doi:10.1117/12.2684512

1 August 2023 Graptolite image classification based on feature transfer and mixup data enhancement

Xiaoxiao Xu, Chaofeng Li, Honglin Liu

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 1275409 (2023) https://doi.org/10.1117/12.2684512
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Aiming at the problem of special shape of graptolites, insufficient training samples and manual identification of graptolites consumes a lot of time and energy, a new Graptolite image classification based on feature transfer and mixup data enhancement is proposed. First, we expand graptolite dataset by using traditional data enhancement and mixup to generate mixed samples, then use the deep learning model pretrained on the large-scale ImageNet dataset to construct a new transfer learning model through feature transfer. Finally, tenfold cross validation method is used to test the recognition accuracy of the model. The experimental results show that the classification accuracy of the proposed method on the graptolite dataset is 88.65%. Compared with the traditional data enhancement method and the pretrained model trained by ImageNet directly, the classification accuracy of graptolites is improved by 1.19%, which shows that the method has better recognition effect.

Citation Download Citation

Xiaoxiao Xu, Chaofeng Li, and Honglin Liu "Graptolite image classification based on feature transfer and mixup data enhancement", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 1275409 (1 August 2023); https://doi.org/10.1117/12.2684512

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KEYWORDS

Data modeling

Education and training

Image classification

Image enhancement

Machine learning

Statistical modeling

Convolution

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