Loading [MathJax]/extensions/MathMenu.js
Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning | IEEE Journals & Magazine | IEEE Xplore

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning


This paper proposed a dynamic weight multi-model fusion method for fine-grained image classification based on transfer learning, and experimentally verified its effective...

Abstract:

Current single-model methods for fine-grained image classification suffer from insufficient generalisation ability, while multi-model fusion methods suffer from weight cu...Show More

Abstract:

Current single-model methods for fine-grained image classification suffer from insufficient generalisation ability, while multi-model fusion methods suffer from weight curing. The study suggests and experimentally tests a dynamic weight multi-model fusion strategy for transfer learning-based fine-grained picture classification. The results of the experiment showed that the suggested fusion model enhanced recognition accuracy by 1.33%, 1.19%, and 0.83% compared to the single model on the medical dataset and 3.25%, 1.34%, and 7.28% on the agronomy dataset, respectively. Furthermore, when compared to the comparison method, the models under the proposed method of the study improved recognition accuracy by 0.18%, 0.61%, 0.43%, and 0.43% on the medical dataset, and the experimental time consumed was 3.25 minutes less than that of the sum-of-maximum-probabilities method; however, the fusion models of the proposed method of the study had higher recognition accuracy than that of the comparison met Overall, the proposed dynamic weight multi-model fusion method for fine-grained image classification using migration learning has better performance and generalisation ability, which can improve performance while reducing time cost, and has higher application value for the actual fine-grained image classification task.
This paper proposed a dynamic weight multi-model fusion method for fine-grained image classification based on transfer learning, and experimentally verified its effective...
Published in: IEEE Access ( Volume: 12)
Page(s): 31977 - 31987
Date of Publication: 22 February 2024
Electronic ISSN: 2169-3536

Funding Agency:


References

References is not available for this document.