Boosting Prediction of Geo-location for Web Images Through Integrating Multiple Knowledge Sources
Hao Kuang, Shiai Zhu, Abdulmotaleb El Saddik · 2015
Estimating geographical information of a given photo is a challenging task due to the massive spread of candidate locations on the earth. With the help of freely available geo-tagged Web images, the problem can be addressed by propagating geo-coordinates (latitude and longitude) of geo-related training data, which is obtained using document retrieval techniques. The state-of-the-art approach adopts language modeling technique to estimate the probability distribution of image associated tags in a local region. Under this framework, we propose to differentiate the tags based on the knowledge explored from multiple sources. Finally, a set of geo-informative tags are identified and further emphasized during the model learning and geo-location prediction. In addition, accurate geo-coordinates are estimated by incorporating the image visual information. Experiments on a large-scale geo-tagged Flickr image dataset demonstrate the effectiveness of proposed method at different levels of evaluation granularity.