Visual place recognition is the task of recognizing a place depicted in an image based on its pure visual appearance without metadata. In visual place recognition, the challenges lie upon not only the changes in lighting conditions, camera viewpoint, and scale but also the characteristic of scene-level images and the distinct features of the area. To resolve these challenges, one must consider both the local discriminativeness and the global semantic context of images. On the other hand, the diversity of the datasets is also particularly important to develop more general models and advance the progress of the field. In this paper, we present a fully-automated system for place recognition at a city-scale based on content-based image retrieval. Our main contributions to the community lie in three aspects. Firstly, we take a comprehensive analysis of visual place recognition and sketch out the unique challenges of the task compared to general image retrieval tasks. Next, we propose yet a simple pooling approach on top of convolutional neural network activations to embed the spatial information into the image representation vector. Finally, we introduce new datasets for place recognition, which are particularly essential for application-based research. Furthermore, throughout extensive experiments, various issues in both image retrieval and place recognition are analyzed and discussed to give some insights into improving the performance of retrieval models in reality. The dataset used in this paper can be found at https://github.com/canhld94/Daejeon520

翻译：视觉地点识别是一项仅基于图像纯视觉外观（无元数据）来识别图像中所描绘地点的任务。在视觉地点识别中，挑战不仅在于光照条件、摄像机视角和尺度的变化，还在于场景级图像的特性以及区域的独特特征。为解决这些挑战，必须同时考虑图像的局部判别性和全局语义上下文。另一方面，数据集的多样性对于开发更通用的模型并推动该领域的进展也尤为重要。本文提出了一种基于内容图像检索的全自动城市级地点识别系统。我们对社区的主要贡献体现在三个方面。首先，我们对视觉地点识别进行了全面分析，并勾勒出该任务相较于一般图像检索任务的独特挑战。其次，我们提出了一种在卷积神经网络激活基础上嵌入空间信息到图像表示向量的简单池化方法。最后，我们引入了新的地点识别数据集，这对基于应用的研究尤为关键。此外，通过大量实验，我们分析并讨论了图像检索和地点识别中的各种问题，为在现实中提升检索模型性能提供了见解。本文使用的数据集可在 https://github.com/canhld94/Daejeon520 获取。