Location-based image retrieval for urban environments

Image based localization is an important problem with many applications. The basic idea is to match a user generated query image against a database of geo-tagged images with known 6 degrees of freedom poses. Once this retrieval problem is solved, it is possible to recover the pose of the query image. A challenging problem in image retrieval is performance degradation as the size of the image database grows. In this paper we describe an approach to large scale image retrieval for user localization in urban environment by taking advantage of coarse position estimates available, e.g. via cell tower triangulation, on many mobile devices today. The basic idea is to partition the large image database for a large region into a number of overlapping cells each with its own prebuilt search and retrieval structure. We demonstrate retrieval results over a ∼12,000 image database covering a 1 km2 area of downtown Berkeley.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  Wei Zhang,et al.  Image Based Localization in Urban Environments , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[3]  Richard Szeliski,et al.  City-Scale Location Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Bernd Girod,et al.  Outdoors augmented reality on mobile phone using loxel-based visual feature organization , 2008, MIR '08.

[5]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[6]  Pietro Perona,et al.  Scaling object recognition: Benchmark of current state of the art techniques , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[7]  Bernd Girod,et al.  Robust image retrieval using multiview scalable vocabulary trees , 2009, Electronic Imaging.

[8]  Marc Pollefeys,et al.  Handling Urban Location Recognition as a 2D Homothetic Problem , 2010, ECCV.

[9]  Matthew Turk,et al.  Location-based augmented reality on mobile phones , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.