Region-based landmark discovery by crowdsourcing geo-referenced photos

We propose a novel model for landmark discovery that locates region-based landmarks on map in contrast to the traditional point-based landmarks. The proposed method preserves more information and automatically identifies candidate regions on map by crowdsourcing geo-referenced photos. Gaussian kernel convolution is applied to remove noises and generate detected region. We adopt F1 measure to evaluate discovered landmarks and manually check the association between tags and regions. The experiment results show that more than 90% of attractions in the selected city can be correctly located by this method.