Abstract:

Aiming at the complexity and diversity of fine-grained images， where traditional image classification methods exhibit limitations in focusing on fine-grained attributes and perform poorly when handling imbalanced datasets， a threshold-based fine-grained image classification algorithm utilizing deep metric learning was proposed. The focus on fine-grained attributes of images was enhanced by introducing a metric learning approach. Additionally， the classification accuracy was enhanced and the model convergence was expedited by incorporating pairwise loss and agent loss mechanisms. To address the issue of data imbalance， a classifier was devised grounded in threshold analysis techniques. This innovative classifier harnesses threshold analysis to facilitate multi-level classification of fine-grained images， thereby ameliorating the issue of low classification accuracy for certain categories within an imbalanced dataset. The results of these experiments unequivocally demonstrate that the proposed threshold classification algorithm for fine-grained images， based on deep metric learning， outperforms alternative methods in terms of classification accuracy.

Key words: computer application, deep metric learning, fine-grained classification, unbalanced data, threshold classifier