With recent advances in mobile computing, the demand for visual localization or landmark identification on mobile devices is gaining interest. We advance the state of the art in this area by fusing two popular representations of street-level image data - facade-aligned and viewpoint-aligned - and show that they contain complementary information that can be exploited to significantly improve the recall rates on the city scale. We also improve feature detection in low contrast parts of the street-level data, and discuss how to incorporate priors on a user's position (e.g. given by noisy GPS readings or network cells), which previous approaches often ignore. Finally, and maybe most importantly, we present our results according to a carefully designed, repeatable evaluation scheme and make publicly available a set of 1.7 million images with ground truth labels, geotags, and calibration data, as well as a difficult set of cell phone query images. We provide these resources as a benchmark to facilitate further research in the area.

Recent approaches to reconstructing city-sized areas from large image collections usually process them all at once and only produce disconnected descriptions of image subsets, which typically correspond to major landmarks. In contrast, we propose a framework that lets us take advantage of the available meta-data to build a single, consistent description from these potentially disconnected descriptions. Furthermore, this description can be incrementally updated and enriched as new images become available. We demonstrate the power of our approach by building large-scale reconstructions using images of Lausanne and Prague.

Users expect efficient ways of handling their personal audio and image data using only the mobile device. Data sharing with friends and interest groups is also becoming increasingly important. Browsing and manually searching through your own and a shared data library can be difficult. For example, the simplest traditional attributes like time and date are normally unknown for other people's data. In this paper a search application running on mobile device is described. The application relies on automatic content analysis and metadata generation for audio and image data. Two search paradigms - class based search and example based search - are supported. In mobile devices the small display size makes it difficult to handle large amounts of information quickly. For this reason efficient metadata visualization is important. In our system the content metadata is converted into visual form that is fast to browse through at one glance.