With the rapid development of deep learning techniques, convolutional neural networks (CNN) have been widely investigated for the feature representations in the image retrieval task. However, the key step in CNN-based retrieval, i.e., feature aggregation has not been solved in a robust and general manner when tackling different kinds of images. In this paper, we present a deep feature aggregation method for image retrieval using the Fourier transform and low-pass filtering, which can adaptively compute the weights for each feature map with discrimination. Specifically, the low-pass filtering can preserve the semantic information in each feature map by transforming images to the frequency domain. In addition, we develop three adaptive methods to further improve the robustness of feature aggregation, i.e., Region of Interests (ROI) selection, spatial weighting and channel weighting. Experimental results demonstrate the superiority of the proposed method in comparison with other state-of-the-art, in achieving robust and accurate object retrieval under five benchmark datasets.

随着深度学习技术的飞速发展，卷积神经网络（CNN）已被广泛研究用于图像检索任务中的特征表示。但是，在处理不同种类的图像时，尚未以健壮和通用的方式解决基于CNN的检索中的关键步骤，即特征聚合。在本文中，我们提出了一种使用傅里叶变换和低通滤波进行图像检索的深度特征聚合方法，该方法可以自适应地计算出每个特征图的权重。具体而言，低通滤波可以通过将图像变换到频域来在每个特征图中保留语义信息。此外，我们开发了三种自适应方法来进一步提高特征聚合的鲁棒性，即感兴趣区域（ROI）选择，空间权重和渠道权重。实验结果表明，与其他现有技术相比，该方法在五个基准数据集下实现鲁棒且准确的对象检索方面具有优越性。