Abstract
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.
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Cao, L., Gao, Y., Liu, Q., Ji, R. (2013). Geographical Retagging. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_5
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DOI: https://doi.org/10.1007/978-3-642-35728-2_5
Publisher Name: Springer, Berlin, Heidelberg
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