基于原型嵌入图网络的小样本图像分类

Few-shot image classification based on prototype embedding graph network

针对在小样本图像分类中传统骨干卷积网络进行特征提取时会有上下文信息单一和感受野受限以及边特征相似度度量缺乏全局性问题,本文提出了一种基于原型嵌入图网络的小样本图像分类算法。首先,将CBAM产生的权重值与ASPP以不同采样率获取的不同尺度特征进行相乘的特征作为图网络的节点嵌入特征。然后,采用原型网络的方法在度量模块中构建了原型节点,使得成对节点之间的相似性计算转化为单个节点与原型节点相似性的和计算,将得到的相似度作为边特征输入图神经网络。最后,利用双图结构在多个更新代后将标签信息从有标签样本传播到无标签样本。在以ResNet-12为骨干卷积网络的算法分类任务中,本文在mini ImageNet、tiered ImageNet、CUB-200-2011和CIFAR-FS 4个数据集上的5-way 1-shot的任务分类准确率分别达到了71.47%、75.41%、86.21%和79.84%,在以Conv-4作为骨干卷积网络中,本文提出的算法在5-way 1-shot和5-way 5-shot任务中都优于现有的图网络方法。

Traditional backbone convolutional networks for feature extraction in few-shot image classification suffer from the problems of single context information,limited receptive field,and lack of global edge feature similarity measurement.In this paper,we propose a few-shot image classification algorithm based on prototype embedding graph network.First,the feature obtained by multiplying the weight values generated by CBAM with the features of different scales obtained by ASPP at different sampling rates is used as the node embedding feature of the graph network.Then,the prototype nodes are constructed in the measurement module using the prototype network method,transforming the similarity calculation between paired nodes into the sum of the similarity between a single node and the prototype node,and using the obtained similarity as the edge feature input to the graph neural network.Finally,the label information is propagated from labeled samples to unlabeled samples through the dual graph structure after multiple update iterations.In the classification task using ResNet-12 as the backbone convolutional network,our algorithm achieves classification accuracies of 71.47%,75.41%,86.21%,and 79.84%on the mini ImageNet,tiered ImageNet,CUB-200-2011,and CIFAR-FS datasets,respectively,for the 5way-1shot task.In the backbone convolutional network using Conv-4,our proposed algorithm outperforms existing graph network methods in both 5way-1shot and 5way-5shot tasks.