Is it possible to determine the location of a photo from just its pixels? While the general problem seems exceptionally difficult, photos often contain cues such as landmarks, weather patterns, vegetation, road markings, or architectural details, which in combination allow to infer where the photo was taken. Previously, this problem has been approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, this model achieves a 50 performance improvement over the single-image model.
Image retrieval based on local features, bags-of-visual-words (BoVWs) and inverted indices @cite_17 @cite_43 has been shown to be more accurate than global descriptors at matching buildings, but requires more space and lacks the invariance to match natural scenes or articulated objects. Most local feature based approaches therefore focus on localization within cities, either based on photos from photo sharing websites @cite_20 @cite_40 or street view @cite_46 @cite_35 @cite_34 @cite_12 @cite_48 @cite_1 @cite_5 . Skyline2GPS @cite_26 also uses street view data, but takes a unique approach that segments the skyline out of an image captured by an upward-facing camera and matches it against a 3D model of the city.
@cite_35 @cite_26 @cite_48 @cite_1 @cite_43 @cite_40 @cite_5 @cite_46 @cite_34 @cite_12 @cite_20 @cite_17
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Local descriptors are mostly extracted over the raw intensity values of the image and requires the analysis of patterns in local pixel neighborhoods. The raw images don't provide much local relationship information required for the local descriptors. To resolve this problem, a new method of local image feature description is proposed in this paper based on the patterns extracted from the multiple filtered images. The whole process is structured as follows: First, the images are filtered with the bag of filters (BoF) and then local binary pattern (LBP) is computed over each filtered image and finally concatenated to find out the single BoF-LBP descriptor. The local information enriches in the filtered images improves the discriminative ability of the final descriptor. Content based image retrieval (CBIR) experiments are performed to observe the effectiveness of the proposed approach and compared with the recent state-of-the-art methods. The experiments are conducted over four benchmark databases having natural and texture images such as Corel-1k, Corel-10k, MIT-VisTex and STex-512S. The experimental results confirm that the introduced approach is able to improve the retrieval performance of the CBIR system. The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or “bird's eye” imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations. In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50 ) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results. This paper presents a new approach for the challenging problem of geo-localization using image matching in a structured database of city-wide reference images with known GPS coordinates. We cast the geo-localization as a clustering problem of local image features. Akin to existing approaches to the problem, our framework builds on low-level features which allow local matching between images. For each local feature in the query image, we find its approximate nearest neighbors in the reference set. Next, we cluster the features from reference images using Dominant Set clustering, which affords several advantages over existing approaches. First, it permits variable number of nodes in the cluster, which we use to dynamically select the number of nearest neighbors for each query feature based on its discrimination value. Second, this approach is several orders of magnitude faster than existing approaches. Thus, we obtain multiple clusters (different local maximizers) and obtain a robust final solution to the problem using multiple weak solutions through constrained Dominant Set clustering on global image features, where we enforce the constraint that the query image must be included in the cluster. This second level of clustering also bypasses heuristic approaches to voting and selecting the reference image that matches to the query. We evaluate the proposed framework on an existing dataset of 102k street view images as well as a new larger dataset of 300k images, and show that it outperforms the state-of-the-art by 20 and 7 percent, respectively, on the two datasets.