Fine-Grained Image Classification Method Based on Convolutional Semantic Enhancement
Fine-grained image classification refers to the finer-grained subcategory division based on the divided basic categories.Due to the data features of small inter class differences and large intra class differences in fine-grained image classification,it has become a very chal-lenging research task.Through the analysis and research of existing fine-grained image classification algorithms and models,a weakly super-vised fine-grained classification method based on convolution-enhanced multi-scale feature semantics is proposed.This method correlates high-level and low-level features through convolution,uses high-level feature semantics,highlights underlying meaningful features,sup-presses low-level features with invalid semantics,and obtains multi-scale features with more expressive capabilities.Based on the ResNeXt-101 network as the backbone network and the feature extraction network,the method is verified experimentally on three commonly used fine-grained image datasets,and the classification accuracy rates are 88.3％,93.7％and 94.3％respectively.Experimental results show that this method achieves better classification results than other mainstream fine-grained classification algorithms such as the semantics method(SEF)which enhances the sub-features of global features,and multi-layer feature fusion method(MFF)which uses parallel convolution blocks.
万方数据
维普
