You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Place recognition: An Overview of Vision Perspective 
Chapter Title:  DISCUSSION AND FUTURE WORK 
1. Abstract of Cited Paper (BIB001):  Conventional methods for indoor scenes classification is a challenging task due to the gaps between images? visual features and semantics. These methods do not consider the interactions among features or objects. In this paper, a novel approach is proposed to classify scenes by embedding semantic information in the weighted hypergraph learning. First, hypergraph regularization is improved by optimizing weights of hyperedges. Second, the connectivity among images is learned by statistics of objects appearing in the same image. In this way, semantic gap is narrowed. The experimental results demonstrate the effectiveness of the proposed method. A novel approach to classify scenes by embedding semantic information.A new hypergraph regularization by optimizing weights of hyperedges.Constructing connectivity among images by using statistics of objects appearing in the same image. 
2. Abstract of Cited Paper (BIB002):  Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines—particularly recognition in computer vision and animal navigation in neuroscience—have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition—the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.

Literature Review:
Place recognition has made great advances in the last few decades, e.g., the principles of how animals recognize and remember places and relationship between places in a neuroscience perspective BIB003 , a new way of describing places using convolutional neural networks, a number of datasets specifically for places are put forward. However, we are still a long way from a robust long-term visual place recognition system which can be well applied to a variety of scenarios of real-world places. Hence, we highlight several promising avenues of ongoing and future research that are leading us closer to this ultimate outcome. Place recognition is becoming a hot research field and benefiting from related ongoing works in other fields, especially the enormous successes achieved in computer vision through deep learning technique, e.g., image classification, object detection, scene recognition. While features extracted from pre-trained CNNs on other vision tasks are shown to be transferable and versatile, theyve so far yielded unsatisfactory performance on place recognition task. There is a high possibility that we still dont fully exploit the potential of CNNs, we can improve the performance in two aspects. First, gather a sufficient amount of place-centric data covering various environments including illumination, weather, structure, season and viewpoint change. One alternative and reliable source is Google Street View Time Machine. If you train a CNN on a small-size dataset, the model usually works awful on other datasets since place recognition is dataset-dependent. And one needs to retrain it when a new dataset is fed into it. Since one of advantages of CNNs is to extract representative features through Big data, state-of-the-art performance can be improved. Second, an optimized CNN architecture for place recognition task. Real-world images from cameras are usually high resolution, whereas in many cases one needs to downscale the original images. For example, the input size of vgg-16 is 224*224. Moreover, an architecture that is well-suited for object detection may not fit well into place recognition task since their visual cues are different. And designing a good loss function is essential to features. Developments focused on the above two problems will further improve the robustness and performance of existing deep learning-based place recognition techniques. Place recognition system can also benefit from ongoing researches of object detection, scene classification BIB001 and scene recognition BIB002 . Semantic context from scene, interpreted as the gist of the scene, can help to partition the search space when comparing similarity between image representations, which ensures scalability and real-time processing towards real-world application. Note that different places may share a common semantic concept, which needs a furthermore and precise feature mapping procedure. Objects such as pedestrian and trees should be avoided, while objects like buildings and landmarks are important for long-term place recognition. Automatically determining and suppressing features that leads to confusion to visual place recognition systems will also improve the place recognition performance.