Fine-Grained Object Recognition Method Based on Graph Bilinear Pooling Feature Encoding
To address the problems of visual burst and feature redundancy in the classical bilinear poo-ling model in the field of fine-grained image recognition,this paper proposes a graph bilinear pooling mod-el.This model integrates graph networks into the bilinear pooling framework,leveraging the aggregation capabilities of graph networks to encode differential image features into higher-order features,thereby alle-viating the phenomenon of visual burst during the encoding process.The results of the experiments con-ducted on the three public datasets of CUB,Cars and Aircrafts show that the proposed model achieves ac-curacies of 87.8％,93.5％and 89.6％,respectively.Compared with decomposed bilinear pooling,this model's parameter count is only 25％of the baseline model,while the recognition accuracy is improved by 2.4％,1.7％,and 1.3％,respectively,which fully verifies the effectiveness of the model and can provide a method reference for fine-grained recognition of military targets.
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