Fine-grained image classification algorithm combining saliency and non-local module
Aiming at the problems of inaccurate discriminative feature acquisition and insufficient use of training data in fine-grained image classification tasks,a fine-grained image classification algorithm combining significance and non-local modules is proposed.By clipping the significance region of four training images and stitching them into one training image,the training data could be enriched and enhanced.Moreover,the non-local module was embedded into the bottleneck module in the high-dimensional feature layer of the ResNet-50 model to connect the four salience regions of the enhanced image,which strengthened the model's attention to the global context information.On the Stanford Cars and CUB-200-2011 data sets,the accuracy of Top-1 classification was 94.01%and 85.97%,respectively.This method performs better than compared data augmentation methods and fine-grained image classification algorithms.
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