基于滑动特征向量的小样本图像分类方法

Few⁃shot image classification method based on sliding feature vectors

针对在小样本图像分类中,几个样本的特征图不足以描述整个类特征空间,导致误分类的问题,提出了滑动特征向量神经网络(SFV),该方法通过集合同类样本的滑动特征向量构建类特征空间,并利用样本-类的特征向量度量方式分类查询样本。SFV融合了特征块的边缘信息以及位置结构的相关性,最大限度地利用深层特征信息的同时扩充了类特征空间。实验表明:在各数据集中SFV均能取得不错的效果,在细粒度数据集上,达到了最佳精度。

In the task of few-shot image classification,the extremely limited number of labeled examples per class can hardly represent the real class distribution effectively,which is the main reason for misclassification.To tackle this problem,we propose a method which named Sliding Feature Vectors Neural Network(SFV).The method aims to assemble all the local sliding feature vectors of samples from the same class to construct the class-level feature spaces,and then it utilized the image-to-class measure to classify the query samples.That means on the measure stage,SFV compare the similarity between the class and the query sample.SFV expands the class feature space by adding the edge information of feature blocks and correlation of their position and structures to maximize the utilization of the deep feature maps when the sample is extremely limited,which can ease overfitting problem caused by small sample data.Experimental study on benchmark datasets consistently shows its superiority over the related other framework,especially on fine-grained datasets,it achieves state-of-the-art.