# Place Recognition: An Overview of Vision Perspective
## 1 Introduction  
Place recognition has attracted a significant amount of attention in the computer-vision and robotics communities, as evidenced by the related citations and a number of workshops dedicated to improving long-term robot navigation and autonomy [1]. It has a number of applications, ranging from autonomous driving and robot navigation to augmented reality and geolocalizing archival imagery. The process of identifying the location of a given image by querying the locations of images belonging to the same place in a large geotagged database, usually known as place recognition, is still an open problem. One major characteristic that separates place recognition from other visual-recognition tasks is that place recognition has to solve condition-invariant recognition to a degree that many other fields haven't. How can we robustly identify the same real-world place undergoing major changes in appearance (e.g., illumination variation (Figure 1), change of seasons (Figure 2) or weather, structural modifications over time, and viewpoint change)? To be clear, the above changes in appearance are summarized as conditional variations, but exclude viewpoint change. Moreover, how can we distinguish true images from similar-looking images without supervision? Since collecting geotagged datasets is time-consuming and labor-intensive, and locations like indoor places do not necessarily have GPS information. Place-recognition tasks have been traditionally cast as image-retrieval tasks [2] where image representations for places are essential. The fundamental scientific question is what theappropriate representation of a place is that is informative enough to recognize real-world places, yet compact enough to satisfy the real-time processing requirement on a terminal, such as a mobile phone or a robot. At early stages, place recognition was dominated by sophisticated local-invariant feature extractors, such as Scale-Invariant Feature Transformation (SIFT) [3] and Speed-Up Robust Features (SURF) [4], hand-crafted global image descriptors, such as Generalized Search Trees (GIST) [5; 6], and the bag-of-visual-words [7; 8] approach. These traditional feature-extraction techniques have gained impressive results. Recent years have seen a prosperous advancement of visual content recognition using a powerful image representation-extractor--Convolutional Neural Networks (CNNs) [9; 10; 11; 12], which offers state-of-the-art performance on many category-level recognition tasks, such as object classification [9; 10; 13], scene recognition [14; 15; 16], and image classification [17]. The principle ideas of CNNs date back to 1980s, and the two major reasons why CNNs are so successful in computer vision are the advances in GPU-based computation power and data volume, respectively. Recent studies show that general features extracted by CNNs can be transferable [18] and generalized well to other visual-recognition tasks. The semantic gap is a well-known problem in place recognition, where different semantics of places may share common low-level features extracted by SIFT, e.g., colors and textures. Convolutional neural networks may bridge this semantic gap by treating an image as a high-level feature vector extracted through deep-stacked layers. This paper provides an overview of both traditional and deep-learning-based descriptive techniques widely applied to place-recognition tasks, which is by no means exhaustive. The remainder of this paper is organized as follows. Section 2 talks about local and global image descriptors that are widely applied to place recognition. Section 3 presents a brief view of convolutional neural networks and the corresponding techniques used in place recognition. Section 4 discusses future work on place recognition.The primary abbreviations and their extended representations in the paper are listed in Table 1.  
Figure 1: TokyoTimeMachine dataset [19] images from the same place with different illumination conditions: day, sunset, night.