一种基于胶囊网络的图像检索方法

A Method for Image Retrieval with Capsule Network

作为一种新兴的网络结构,胶囊网络用向量输出替代标量输出,能够捕捉图像特征之间的空间关系,改善卷积神经网络的局限性。首先对胶囊网络进行训练实现图像分类,得到图像的预测类标签,判定出查询图像的所属类别,然后将网络的数字胶囊层中的特征参数作为图像的特征向量,在查询图像的所属类别集合中利用图像特征向量找到与查询图像相似的图像。分别在FASHION-MNIST和CIFAR10数据集上进行了实验,实验结果表明本文方法可以较好地提取出图像的特征,分别提升了查准率,并取得了良好的图像检索结果。

As an emerging network structure,the capsule network uses vector output instead of scalar output,which can capture the spatial relationship between image features and improve the limitations of convolutional neural network.This paper firstly trains the capsule network to achieve image classification,obtains the predictive label of the image,determines the category of the query image,and then uses the feature parameters in the digital capsule layer of the network as the feature vector of the image.The feature vector is used to find images similar to the query image in the category set of the query image.In this paper,experiments are carried out on the FASHION-MNIST and CIFAR10 datasets respectively.The experimental results show that the proposed method can better extract the features of the images and obtain good image retrieval results.