Content-based image retrieval is the process of retrieving a subset of images
from an extensive image gallery based on visual contents, such as color, shape
or spatial relations, and texture. In some applications, such as localization,
image retrieval is employed as the initial step. In such cases, the accuracy of
the top-retrieved images significantly affects the overall system accuracy. The
current paper introduces a simple yet efficient image retrieval system with a
fewer trainable parameters, which offers acceptable accuracy in top-retrieved
images. The proposed method benefits from a dilated residual convolutional
neural network with triplet loss. Experimental evaluations show that this model
can extract richer information (i.e., high-resolution representations) by
enlarging the receptive field, thus improving image retrieval accuracy without
increasing the depth or complexity of the model. To enhance the extracted
representations' robustness, the current research obtains candidate regions of
interest from each feature map and applies Generalized-Mean pooling to the
regions. As the choice of triplets in a triplet-based network affects the model
training, we employ a triplet online mining method. We test the performance of
the proposed method under various configurations on two of the challenging
image-retrieval datasets, namely Revisited Paris6k (RPar) and UKBench. The
experimental results show an accuracy of 94.54 and 80.23 (mean precision at
rank 10) in the RPar medium and hard modes and 3.86 (recall at rank 4) in the
UKBench dataset, respectively.

本文提出了一种使用三元组损失（triplet loss）的卷积神经网络方法，该方法采用膨胀残差卷积神经网络（dilated residual convolutional neural network）提取高分辨率表示，并通过广义均值池化（Generalized-Mean pooling）方法增强提取的表示的鲁棒性，该方法在 Revisited Paris6k 和 UKBench 等数据集上取得良好的图像检索精度。