Multi-model Fusion Fine-grained Image Classification Method Based on Migration Learning

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Zhang, Wenying, Wang, Yaping
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Agronomy; Analytical models; Classification; CNN; Computational modeling; Convolutional neural networks; Datasets; Feature extraction; Fine-grained image; Generalization capability; Grain boundaries; Image classification; Image recognition; Learning; Learning systems; Mathematical models; Migration learning; Model accuracy; Multi-model; Performance enhancement; Recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3369032

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.