Place recognition: An Overview of Vision Perspective <s> DISCUSSION AND FUTURE WORK <s> Recently, we have witnessed a surge of interests of learning a low-dimensional subspace for scene classification. The existing methods do not perform well since they do not consider scenes' multiple features from different views in low-dimensional subspace construction. In this paper, we describe scene images by finding a group of features and explore their complementary characteristics. We consider the problem of multiview dimensionality reduction by learning a unified low-dimensional subspace to effectively fuse these features. The new proposed method takes both intraclass and interclass geometries into consideration, as a result the discriminability is effectively preserved because it takes into account neighboring samples which have different labels. Due to the semantic gap, the fusion of multiview features still cannot achieve excellent performance of scene classification in real applications. Therefore, a user labeling procedure is introduced in our approach. Initially, a query image is provided by the user, and a group of images are retrieved by a search engine. After that, users label some images in the retrieved set as relevant or irrelevant with the query. The must-links are constructed between the relevant images, and the cannot-links are built between the irrelevant images. Finally, an alternating optimization procedure is adopted to integrate the complementary nature of different views with the user labeling information, and develop a novel multiview dimensionality reduction method for scene classification. Experiments are conducted on the real-world datasets of natural scenes and indoor scenes, and the results demonstrate that the proposed method has the best performance in scene classification. In addition, the proposed method can be applied to other classification problems. The experimental results of shape classification on Caltech 256 suggest the effectiveness of our method. Highlights? We describe scene images through multimodal features. ? We explore the complementary characteristics of these features. ? We address multimodal dimensionality reduction by learning a unified low-dimensional subspace. ? We adopt alternating optimization to integrate the features with the user labeling information. ? We develop a novel multimodal dimensionality reduction method for scene classification. <s> BIB001 </s> Place recognition: An Overview of Vision Perspective <s> DISCUSSION AND FUTURE WORK <s> 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. <s> BIB002 </s> Place recognition: An Overview of Vision Perspective <s> DISCUSSION AND FUTURE WORK <s> 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. <s> BIB003
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.