Abstract
There are increasingly large amounts of imagery and video collected from a variety of sensor modalities. Considering that each individual image may contain considerable amounts of information, the ability to interpret, understand and extract scene information is highly beneficial for many communities such as online social networking sites, intelligence agencies and companies dealing with large-scale data mining. In order to enable automated scene understanding, there is a need for an organizing principle to store, visualize and exploit the data. Three-dimensional geometry provides such an organizing principle as imagery and video have inherent 3D structure and can be associated with geographic coordinates. Imagery with geo-spatial information can be used to develop a common 3D world model representation that integrates data across a wide variety of sensor modalities. In this paper, we leverage multiple large geo-spatial databases to create a 3D world model and develop a hierarchical image geo-location framework using a coarse-to-fine approach to geo-locate a query image. Starting at the coarsest level, we developed a novel method to geo-locate images to regions of the world through a process of terrain classification. Next, we developed novel medium-scale and fine-scale localization steps to rule out most of the coarsely geo-located regions and determine candidate geo-locations with geo-positioning accuracy at a city level. Our method was demonstrated on a 6.5 million image database and shown to improve on current state of the art in the areas of both terrain classification and image geo-location.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, W., Kosecka, J.: Image Based Localization in Urban Environments. In: 3DPVT 2006 (2006)
Zamir, A.R., Shah, M.: Accurate Image Localization Based on Google Maps Street View. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 255–268. Springer, Heidelberg (2010)
Pollefeys, M., Nister, D., Frahm, J., Akbarzadeh, A., Mordo-hai, P., Clipp, B., Engels, C., Gallup, D., Kim, S., Merrell, P.: Detailed Real-Time Urban 3D Reconstruction from Video. IJCV 78(2-3), 143–167 (2008)
Snavely, N.: Scene Reconstruction and Visualization from Internet Photo Collections. Doctoral thesis, University of Washington (2008)
Hays, J., Efros, A.A.: IM2GPS: estimating geographic information from a single image. In: CVPR 2008 (2008)
Hays, J.: Large Scale Scene Matching for Graphics and Vision. CMU PhD Thesis (2009)
Bellman, R.E.: Dynamic programming. Princeton University Press, Rand Corporation (1957) ISBN 978-0-691-07951-6
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
De Bonet, J.S., Viola, P.: Structure driven image database retrieval. In: Advances in Neural Information Processing, vol. 10, pp. 866–872 (1997)
Oliva, A., Torralba, A.: Building the gist of a scene: The role of global image features in recognition. In: Visual Perception. Progress in Brain Research, vol. 155 (2006)
Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: CVPR, vol. 2, pp. 2161–2168 (2006)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)
Liu, C., Yuen, J., Torralba, A.: Nonparametric scene parsing: label transfer via dense scene alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009)
Oliva, A., Torralba, A.: Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. International Journal of Computer Vision 42(3) (2001)
Global Land Cover Characterization Database, http://edc2.usgs.gov/glcc/glcc.php
UNEP, Mountains and Tree cover in Mountain Regions (2002), http://www.unep-wcmc.org/mountains-and-tree-cover-in-mountain-regions-2002_724.html
Zhang, H., Berg, A.C., Maire, M., Malik, J.: Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: CVPR 2006 (2006)
Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: CVPR 2008 (2008)
Domeniconi, C., Gunopulos, D.: Adaptive nearest neighbor classification using support vector machines. In: NIPS (2001)
Torralba, A., Fergus, R., Freeman, W.T.: Tiny images. MIT-CSAIL-TR-2007-024 (2007)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. ICCV (July 2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vasile, A.N., Camps, O. (2013). Hierarchical Image Geo-location on a World-Wide Scale. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-41939-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
eBook Packages: Computer ScienceComputer Science (R0)