Experiments in place recognition using gist panoramas

In this paper we investigate large scale view based localization in urban areas using panoramic images. The presented approach utilizes global gist descriptor computed for portions of panoramic images and a simple similarity measure between two panoramas, which is robust to changes in vehicle orientation, while traversing the same areas in different directions. The global gist feature has been demonstrated previously to be a very effective conventional image descriptor, capturing the basic structure of different types of scenes in a very compact way. We present an extensive experimental validation of our panoramic gist approach on a large scale Street View data set of panoramic images for place recognition or topological localization.