Geographical Retagging

While the geographical tag has brought a novel insight into the multimedia content analysis and understanding, how to improve the tagging accuracy has been rarely exploited. In this paper, we present a novel geographical retagging algorithm to improve the inaccurate geographical tags from an automatic photo content based association and refinement perspective. We do not resort to the time-consuming camera pose estimation and scene geometry recovery schemes like structure-from-motion. Instead, our algorithm is deployed based on a very simple neighbor statistical significance test, i.e., geographically nearby images, if near duplicate, should follow a more smooth affine transform comparing with those farther aways. Such an assumption is robust to noisy photo contents caused by multiple factors, such as indoor/outdoor changes, occlusions, or viewing angle changes. It is also very fast comparing to alternative approaches like structure-from-motion or simultaneous localization and matching. We have shown the accuracy, efficiency, and robustness of the proposed retagging algorithm for refining the geographical tags of Flickr images.