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一个基于视觉内容(如颜色、形状或空间关系)和纹理,从一个广泛的图像库中检索一组图像的过程。在有些应用程序中,如本地化,图像检索是作为初始步骤使用。在这种情况下,最高级检索图像的准确性会大大影响整个系统的准确性。当前文件引入了一个简单而有效的图像检索系统,其可训练参数较少,为顶级检索图像提供可接受的准确性。拟议方法得益于一个具有三重损失的扩展残余革命神经网络。实验性评估显示,这一模型可以通过扩大可接收字段来获取更丰富的信息(即高分辨率表示方式),从而在不增加模型深度或复杂性的情况下提高图像检索的准确性。为了提高提取图像的准确性,目前的研究从每个地貌地图中获取了感兴趣的候选区域,并在各区域应用通用-MEan联合。在三重基网络中选择的三重精度影响模型培训,我们采用三重线在线采矿方法。我们用三重线级在线采集信息(即高分辨率表示高分辨率表示格式)在两种英国的硬度模型中,我们测试了RP 和双级数据格式下,在两种RP 上,在两个RP-RB 上,在两个RB 上,在两个RB 上,在两个RB 上,分别测试了两种R-R-R-R-R-R-A-R-R-R-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-R-R-R-R-A-A-R-A-A-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-R-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-R-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L</s>