基于语义特征传播图神经网络的小样本图像分类算法

使用少量样本进行学习和概括的能力是人工智能和人类之间主要的区别。在小样本学习领域,大多数图神经网络专注于将标记的样本信息传递给未标记的查询样本,而忽略了语义特征在分类过程中的重要作用。为此构建了语义特征传播图神经网络,首先将语义特征嵌入到图神经网络中,解决了细粒度图像特征相似性带来的分类准确率低的问题,然后将注意力机制与骨干网络合并达到强化前景并提高特征提取质量的目的,利用马氏距离计算类的相似度得到更好的分类性能,最后使用Funnel ReLU函数作为激活函数进一步提高分类准确率。在基准数据集上实验表明,所提算法相比于基线算法在5类1/2/5样本任务上的准确率分别提高了903%、456%和415%。

Few shot image classification algorithm based on semantic feature propagation graph neural network

The ability to learn and generalize from a small number of samples is a primary distinction between artificial intelligence and humans.In the field of few shot learning,most graph neural networks focus on propagating labeled sample information to unlabeled query samples,while overlooking the crucial role of semantic features in the classification process.To address this,we propose a semantic feature propagation graph neural network,which embeds semantic features into the graph neural network to resolve the issue of low classification accuracy caused by fine grained image feature similarity.We then merge attention mechanisms with backbone networks to strengthen foreground and enhance feature extraction quality.By calculating class similarity using Mahalanobis distance,we achieve superior classification performance.Finally,we utilize the Funnel ReLU function as the activation function to further enhance classification accuracy.Experimental results on benchmark datasets demonstrate that our proposed algorithm improves accuracy by 9.03%,4.56%,and 4.15%,respectively,compared to the baseline algorithm on 5 class 1/2/5 sample tasks.