Xinchao Li
ABSTRACT
Predicting geographic location using exclusively the visual content of images holds the promise of greatly benefiting users' access to media collections. In this paper, we present a visual-content-based approach that predicts where in the world a social image was taken. We employ a ranking method that assigns a query photo the geo-location of its most likely geo-visual neighbor in the social image collection. The novelty of the approach is that ranking makes use not only of the photos themselves, but also their geo-visual neighbors. In contrast to other approaches, we do not restrict the locations we predict to landmarks or specific cities. The approach is evaluated on a set of 3 million geo-tagged photos from Flickr, released by MediaEval 2012. Experiments show that the proposed system delivers a substantive performance improvement compared with previously proposed, related visual content-based approaches. The discussion illustrates how photo densities, geo-visual redundancy and uploader patterns characteristic of social image collections impacts the performance.
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Index Terms
- Geo-visual ranking for location prediction of social images
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