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.

Can a machine tell us if an image was taken in Beijing or New York? Automated identification of the geographical coordinates based on image content is of particular importance to data mining systems, because geolocation provides a large source of context for other useful features of an image. However, successful localization of unannotated images requires a large collection of images that cover all possible locations. Brute-force searches over the entire databases are costly in terms of computation and storage requirements, and achieve limited results. Knowing what visual features make a particular location unique or similar to other locations can be used for choosing a better match between spatially distance locations. However, doing this at global scales is a challenging problem. In this paper we propose an on-line, unsupervised, clustering algorithm called Location Aware Self-Organizing Map (LASOM), for learning the similarity graph between different regions. The goal of LASOM is to select key features in specific locations so as to increase the accuracy in geotagging untagged images, while also reducing computational and storage requirements. Different from other Self-Organizing Map algorithms, LASOM provides the means to learn a conditional distribution of visual features, conditioned on geospatial coordinates. We demonstrate that the generated map not only preserves important visual information, but provides additional context in the form of visual similarity relationships between different geographical areas. We show how this information can be used to improve geotagging results when using large databases.

Detecting visual changes in environments is an important computation with many applications in robotics and computer vision. Security cameras, remotely operated vehicles, and sentry robots could all benefit from robust change detection capability. We conjecture that if one has a mobile camera system the number of visual scenes that are experienced is limited (compared to the space of all possible scenes) and that the scenes do not frequently undergo major changes between observations. These assumptions can be exploited to ease the task of change detection and reduce the computational complexity of processing visual information by utilizing memory to store previous computations. We demonstrate a method to learn the distribution of visual features in an environment via a self-organizing map. Additionally, the spatial distribution of these features can be learned if a positional signal is available. Our method uses a low dimensional representation of visual features to rapidly detect changes in current visual inputs. The model encodes spatially-distributed color histograms of real world visual scenes captured by a camera moved through an environment. The distribution of the color histograms is learned using a self-organizing map with location (when available) and color data. We present tests of the model on detecting changes in an indoor environment.