Mining Crowdsourcing Photos for Recognizing Landmark Areas

Abstract

To solve the problem of automatically drawing landmark areas for nice vista points, in this paper, photos metadata of popular urban landmarks or natural landscapes are collected and extracted from Flickr and Google Map. The Landmark Area Recognition System (LARS) is proposed to efficiently recognize astonishing regions of landmarks for exploring nice visiting and photographic POIs (Point of Interests) of these landmarks. Based on crowdsourcing photos and tags obtained from social networks, LARS implements LBSE (Location-Based Search Engine) for searching near objects efficiently. Next, DBCGM (Density-Based Clustering with Greedy Method) is proposed to cluster the landmark photos into regions. Based on crowdsourcing verifications on the photo-landmark relevance, the data set for experiments were collected for experimental evaluations. The result shows that DBCGM outperforms other density-based clustering methods. Finally, LARS employs the Concave Hull algorithm to draw the landmark area on Google Map as the demonstration of LARS applications.