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.

翻译：基于内容的图像检索是指根据视觉内容（如颜色、形状、空间关系及纹理）从大规模图像库中检索出子集图像的过程。在定位等应用中，图像检索常被用作初始步骤。此时，顶部检索图像的准确性会显著影响整个系统的精度。本文提出了一种简单高效且可训练参数更少的图像检索系统，能在顶部检索图像中实现可接受的准确性。该方法利用带三元组损失的空洞残差卷积神经网络。实验评估表明，该模型通过扩大感受野能提取更丰富的信息（即高分辨率表示），从而在不增加模型深度或复杂度的情况下提升图像检索精度。为增强提取表示的鲁棒性，本研究从每个特征图中获取候选感兴趣区域，并对这些区域应用广义均值池化。由于三元组网络中三元组的选取会影响模型训练，我们采用了三元组在线挖掘方法。我们在两个具有挑战性的图像检索数据集（即Revisited Paris6k（RPar）和UKBench）上，测试了该方法在不同配置下的性能。实验结果显示，在RPar数据集的medium与hard模式下，准确率分别达到94.54和80.23（前10位平均精度），在UKBench数据集中为3.86（前4位召回率）。