Comparative Study of Trust Modeling for Automatic Landmark Tagging
Many images uploaded to social networks are related to travel, since people consider traveling to be an important event in their life. However, a significant amount of travel images on the Internet lack proper geographical annotations or tags. In many cases, the images are tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In this paper, we present a system for automatic geotag propagation in images based on the similarity between image content (famous landmarks) and its context (associated geotags). In such a scenario, however, an incorrect or a spam tag can damage the integrity and reliability of the automated propagation system. Therefore, for reliable geotags propagation, we suggest adopting a user trust model based on social feedback from the users of the photo-sharing system. We compare this socially-driven approach with other user trust models via experiments and subjective testing on an image database of various famous landmarks. Results demonstrate that relying on user feedback is more efficient, since the number of propagated tags more than doubles without loss of accuracy compared to using other models or propagating without trust modeling.