Efficient Image Geotagging Using Large Databases

Abstract— There are now billions of images stored on photo sharing websites. These images contain visual cues that reflect the geographical location of where the photograph was taken (e.g. New York City). Linking visual features in images to physical locations has many potential applications, such as tourism recommendation systems. However, the size and nature of these databases pose great challenges. For example, the distribution of images around the world is highly biased towards popular regions. This results in high redundancy in certain locations, while under-representing the features in other regions. Many density estimation methods are unable to handle such datasets. In this paper we employ an on-line unsupervised clustering method, Location Aware Self-Organizing Map (LASOM), to compress a large image database and learn similarity relationships between different geographical locations. Our method achieves promising results when used on a dataset containing approximately 900,000 images. We further show that the learned representation results in minimal information loss as compared to using k-Nearest Neighbor method. The noise reduction property of LASOM allows for superior performance when combining multiple features. The final part of the paper explores clothing as a new information source that may assist in geolocation of images. < final year projects >