GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor

The number of GPS-tagged images available on the web is increasing at a rapid rate. The majority of such location tags are specified by the users, either through manual tagging or localization-chips embedded in the cameras. However, a known issue with user shared images is the unreliability of such GPS-tags. In this paper, we propose a method for addressing this problem. We assume a large dataset of GPS-tagged images which includes an unknown subset with contaminated tags is available. We develop a robust method for identification and refinement of this subset using the rest of the images in the dataset. In the proposed method, we form a large number of triplets of matching images and use them for estimating the location of the query image utilizing structure from motion. Some of the generated estimations may be inaccurate due to the noisy GPS-tags in the dataset. Therefore, we perform Random Walks on the estimations in order to identify the subset with the maximal agreement. Finally, we estimate the GPS-tag of the query utilizing the identified consistent subset using a weighted mean. We propose a new damping factor for Random Walks which conforms to the level of noise in the input, and consequently, robustifies Random Walks. We evaluated the proposed framework on a dataset of over 18k user-shared images; the experiments show our method robustly improves the accuracy of GPS-tags under diverse scenarios.