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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.