Large Scale Image Retrieval for Location Estimation

The geo-graphical location at which an image or video was taken is a key piece of multimedia information. Such geo-information has become an indispensable component of systems enabling personalized and context-aware multimedia services. The research reported in this thesis investigates how to automatically derive geo-information from multimedia content. In particular, it focuses on the challenge of estimating the geo-coordinates of the location of an image solely on the basis of its visual content. The goal of the research is to develop a scalable visual content-based location estimation system for images and to investigate the possibilities to improve its accuracy and reliability to a substantial extent. The system should be applicable in both the geo-constrained scenario, in which the multimedia item is taken at one of a previously defined set of locations, and the geo-unconstrained scenario, in which the multimedia item could have been taken anywhere in the world. The thesis makes two different kinds of contributions. The first is high-level framework design. We develop a generic large-scale image retrieval-based framework for location estimation.
The second is optimization of specific components of the system. We develop two approaches, geometric verification and geo-distinctive visual element matching, that address specific challenges faced by our retrieval-based framework. The resulting system makes location estimation more tractable in case of large image collections, and also more reliable. Our experimental results demonstrate that the system leads to an overall significant improvement of the location estimation performance and redefines the state-of-the art in both geo-constrained and geo-unconstrained location estimation. Based on the findings presented in this thesis, we make recommendations for future research directions, which we think are substantial and promising for large scale image retrieval and geo-location estimation.